Context

Here is a list that shows the prime number list up to 10000. Source: easycalculation

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This dataset consists of mathematical question and answer pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models.

## Example questions

 Question: Solve -42*r + 27*c = -1167 and 130*r + 4*c = 372 for r.
 Answer: 4
 
 Question: Calculate -841880142.544 + 411127.
 Answer: -841469015.544
 
 Question: Let x(g) = 9*g + 1. Let q(c) = 2*c + 1. Let f(i) = 3*i - 39. Let w(j) = q(x(j)). Calculate f(w(a)).
 Answer: 54*a - 30

It contains 2 million (question, answer) pairs per module, with questions limited to 160 characters in length, and answers to 30 characters in length. Note the training data for each question type is split into "train-easy", "train-medium", and "train-hard". This allows training models via a curriculum. The data can also be mixed together uniformly from these training datasets to obtain the results reported in the paper. Categories:

• algebra (linear equations, polynomial roots, sequences)
• arithmetic (pairwise operations and mixed expressions, surds)
• calculus (differentiation)
• comparison (closest numbers, pairwise comparisons, sorting)
• measurement (conversion, working with time)
• numbers (base conversion, remainders, common divisors and multiples, primality, place value, rounding numbers)
• polynomials (addition, simplification, composition, evaluating, expansion)
• probability (sampling without replacement)

The CIFAR-10 and CIFAR-100 datasets are labeled subsets of the 80 million tiny images dataset. CIFAR-10 and CIFAR-100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. (Sadly, the 80 million tiny images dataset has been thrown into the memory hole by its authors. Spotting the doublethink which was used to justify its erasure is left as an exercise for the reader.)

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.

The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks.

Baseline results You can find some baseline replicable results on this dataset on the project page for cuda-convnet. These results were obtained with a convolutional neural network. Briefly, they are 18% test error without data augmentation and 11% with. Additionally, Jasper Snoek has a new paper in which he used Bayesian hyperparameter optimization to find nice settings of the weight decay and other hyperparameters, which allowed him to obtain a test error rate of 15% (without data augmentation) using the architecture of the net that got 18%.

Other results Rodrigo Benenson has collected results on CIFAR-10/100 and other datasets on his website; click here to view.

Dataset layout Python / Matlab versions I will describe the layout of the Python version of the dataset. The layout of the Matlab version is identical.

The archive contains the files data_batch_1, data_batch_2, ..., data_batch_5, as well as test_batch. Each of these files is a Python "pickled" object produced with cPickle. Here is a python2 routine which will open such a file and return a dictionary: python def unpickle(file): import cPickle with open(file, 'rb') as fo: dict = cPickle.load(fo) return dict And a python3 version: def unpickle(file): import pickle with open(file, 'rb') as fo: dict = pickle.load(fo, encoding='bytes') return dict Loaded in this way, each of the batch files contains a dictionary with the following elements: data -- a 10000x3072 numpy array of uint8s. Each row of the array stores a 32x32 colour image. The first 1024 entries contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image. labels -- a list of 10000 numbers in the range 0-9. The number at index i indicates the label of the ith image in the array data.

The dataset contains another file, called batches.meta. It too contains a Python dictionary object. It has the following entries: label_names -- a 10-element list which gives meaningful names to the numeric labels in the labels array described above. For example, label_names[0] == "airplane", label_names[1] == "automobile", etc. Binary version The binary version contains the files data_batch_1.bin, data_batch_2.bin, ..., data_batch_5.bin, as well as test_batch.bin. Each of these files is formatted as follows: <1 x label><3072 x pixel> ... <1 x label><3072 x pixel> In other words, the first byte is the label of the first image, which is a number in the range 0-9. The next 3072 bytes are the values of the pixels of the image. The first 1024 bytes are the red channel values, the next 1024 the green, and the final 1024 the blue. The values are stored in row-major order, so the first 32 bytes are the red channel values of the first row of the image.

Each file contains 10000 such 3073-byte "rows" of images, although there is nothing delimiting the rows. Therefore each file should be exactly 30730000 bytes long.

There is another file, called batches.meta.txt. This is an ASCII file that maps numeric labels in the range 0-9 to meaningful class names. It is merely a list of the 10 class names, one per row. The class name on row i corresponds to numeric label i.

The CIFAR-100 dataset This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). Her...

Point Cloud MNIST 2D

This is a simple dataset for getting started with Machine Learning for point cloud data. It take the original MNIST and converts each of the non-zero pixels into points in a 2D space. The idea is to classify each collection of point (rather than images) to the same label as in the MNIST. The source for generating this dataset can be found in this repository: cgarciae/point-cloud-mnist-2D

Format

There are 2 files: train.csv and test.csv. Each file has the columns

label,x0,y0,v0,x1,y1,v1,...,x350,y350,v350

where

• label contains the target label in the range [0, 9]
• x{i} contain the x position of the pixel/point as viewed in a Cartesian plane in the range [-1, 27].
• y{i} contain the y position of the pixel/point as viewed in a Cartesian plane in the range [-1, 27].
• v{i} contain the value of the pixel in the range [-1, 255].

Padding

The maximum number of point found on a image was 351, images with less points where padded to this length using the following values:

• x{i} = -1
• y{i} = -1
• v{i} = -1

Subsamples

To make the challenge more interesting you can also try to solve the problem using a subset of points, e.g. the first N. Here are some visualizations of the dataset using different amounts of points:

50

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F158444%2Fbbf5393884480e3d24772344e079c898%2F50.png?generation=1579911143877077&alt=media" alt="50">

100

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F158444%2F5a83f6f5f7c5791e3c1c8e9eba2d052b%2F100.png?generation=1579911238988368&alt=media" alt="100">

200

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F158444%2F202098ed0da35c41ae45dfc32e865972%2F200.png?generation=1579911264286372&alt=media" alt="200">

351

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F158444%2F5c733566f8d689c5e0fd300440d04da2%2Fmax.png?generation=1579911289750248&alt=media" alt="">

Distribution

This histogram of the distribution the number of points per image in the dataset can give you a general idea of how difficult each variation can be.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F158444%2F9eb3b463f77a887dae83a7af0eb08c7d%2Flengths.png?generation=1579911380397412&alt=media" alt="">

This dataset provides information about the number of properties, residents, and average property values for Range View Road cross streets in Valier, MT.

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File name definitions:

'...v_50_175_250_300...' - dataset for velocity ranges [50, 175] + [250, 300] m/s

'...v_175_250...' - dataset for velocity range [175, 250] m/s

'ANNdevelop...' - used to perform 9 parametric sub-analyses where, in each one, many ANNs are developed (trained, validated and tested) and the one yielding the best results is selected

'ANNtest...' - used to test the best ANN from each aforementioned parametric sub-analysis, aiming to find the best ANN model; this dataset includes the 'ANNdevelop...' counterpart

Where to find the input (independent) and target (dependent) variable values for each dataset/excel ?

input values in 'IN' sheet

target values in 'TARGET' sheet

Where to find the results from the best ANN model (for each target/output variable and each velocity range)?

open the corresponding excel file and the expected (target) vs ANN (output) results are written in 'TARGET vs OUTPUT' sheet

Check reference below (to be added when the paper is published)

https://www.researchgate.net/publication/328849817_11_Neural_Networks_-_Max_Disp_-_Railway_Beams

272,700 two-alternative forced choice responses in a simple numerical task modeled after Tenenbaum (1999, 2000), collected from 606 Amazon Mechanical Turk workers. Subjects were shown sets of numbers length 1 to 4 from the range 1 to 100 (e.g. {12, 16}), and asked what other numbers were likely to belong to that set (e.g. 1, 5, 2, 98). Their generalization patterns reflect both rule-like (e.g. “even numbers,” “powers of two”) and distance-based (e.g. numbers near 50) generalization. This data set is available for further analysis of these simple and intuitive inferences, developing of hands-on modeling instruction, and attempts to understand how probability and rules interact in human cognition.

This dataset provides information about the number of properties, residents, and average property values for Range View Circle cross streets in Rapid City, SD.

This paper addresses the computational methods and challenges associated with prime number generation, a critical component in encryption algorithms for ensuring data security. The generation of prime numbers efficiently is a critical challenge in various domains, including cryptography, number theory, and computer science. The quest to find more effective algorithms for prime number generation is driven by the increasing demand for secure communication and data storage and the need for efficient algorithms to solve complex mathematical problems. Our goal is to address this challenge by presenting two novel algorithms for generating prime numbers: one that generates primes up to a given limit and another that generates primes within a specified range. These innovative algorithms are founded on the formulas of odd-composed numbers, allowing them to achieve remarkable performance improvements compared to existing prime number generation algorithms. Our comprehensive experimental results reveal that our proposed algorithms outperform well-established prime number generation algorithms such as Miller-Rabin, Sieve of Atkin, Sieve of Eratosthenes, and Sieve of Sundaram regarding mean execution time. More notably, our algorithms exhibit the unique ability to provide prime numbers from range to range with a commendable performance. This substantial enhancement in performance and adaptability can significantly impact the effectiveness of various applications that depend on prime numbers, from cryptographic systems to distributed computing. By providing an efficient and flexible method for generating prime numbers, our proposed algorithms can develop more secure and reliable communication systems, enable faster computations in number theory, and support advanced computer science and mathematics research.

The exercise after this contains questions that are based on the housing dataset.

1. How many houses have a waterfront? a. 21000 b. 21450 c. 163 d. 173

2. How many houses have 2 floors? a. 2692 b. 8241 c. 10680 d. 161

3. How many houses built before 1960 have a waterfront? a. 80 b. 7309 c. 90 d. 92

4. What is the price of the most expensive house having more than 4 bathrooms? a. 7700000 b. 187000 c. 290000 d. 399000

5. For instance, if the ‘price’ column consists of outliers, how can you make the data clean and remove the redundancies? a. Calculate the IQR range and drop the values outside the range. b. Calculate the p-value and remove the values less than 0.05. c. Calculate the correlation coefficient of the price column and remove the values less than the correlation coefficient. d. Calculate the Z-score of the price column and remove the values less than the z-score.

6. What are the various parameters that can be used to determine the dependent variables in the housing data to determine the price of the house? a. Correlation coefficients b. Z-score c. IQR Range d. Range of the Features

7. If we get the r2 score as 0.38, what inferences can we make about the model and its efficiency? a. The model is 38% accurate, and shows poor efficiency. b. The model is showing 0.38% discrepancies in the outcomes. c. Low difference between observed and fitted values. d. High difference between observed and fitted values.

8. If the metrics show that the p-value for the grade column is 0.092, what all inferences can we make about the grade column? a. Significant in presence of other variables. b. Highly significant in presence of other variables c. insignificance in presence of other variables d. None of the above

9. If the Variance Inflation Factor value for a feature is considerably higher than the other features, what can we say about that column/feature? a. High multicollinearity b. Low multicollinearity c. Both A and B d. None of the above

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Dataset Description: A Deep Dive into Prime Gap Distribution and Primorial Harmonics Overview: This dataset offers a comprehensive exploration of prime gap distribution, focusing on the intriguing patterns associated with primorials and their harmonics. Primorials, the product of the first n prime numbers, play a significant role in shaping the landscape of prime gaps. By analyzing the distribution of prime gaps and their relation to primorials, we can gain deeper insights into the fundamental structure of prime numbers. Data Structure: * Power of 2: The base-2 exponent. * Gap Size N: The size of the Nth prime gap following the given power of 2. Key Features: * Primorial Harmonics: The dataset highlights the appearance of prime gaps that are multiples of primorials, suggesting a deeper connection between these numbers and the distribution of primes. * Large Prime Gaps: The dataset includes information on exceptionally large prime gaps, which can provide valuable clues about the underlying structure of the number line. * Prime Number Distribution: The distribution of prime numbers within the specified range is analyzed, revealing patterns and anomalies. Potential Applications: * Number Theory Research: * Investigating the role of primorials in shaping prime gap distribution. * Testing conjectures related to the Riemann Hypothesis and the Twin Prime Conjecture. * Exploring the connection between prime gaps and other mathematical concepts, such as modular arithmetic and number theory functions. * Machine Learning and Data Science: * Training machine learning models to predict prime gap sizes, incorporating primorials as features. * Developing algorithms to identify and analyze primorial-related patterns. * Computational Mathematics: * Benchmarking computational resources and algorithms for prime number generation and factorization. * Developing new algorithms for efficient computation of primorials and their harmonics. How to Use This Dataset: * Data Exploration: * Visualize the distribution of prime gaps, highlighting the occurrence of primorial harmonics. * Analyze the frequency of different gap sizes, focusing on multiples of primorials. * Study the relationship between prime gap size and the corresponding power of 2, considering the influence of primorials. * Machine Learning: * Incorporate features related to primorials and their harmonics into machine learning models. * Experiment with different feature engineering techniques and hyperparameter tuning to improve model performance. * Use the dataset to train models that can predict the occurrence of large prime gaps and other significant patterns. * Number Theory Research: * Use the dataset to formulate and test new conjectures about the distribution of prime gaps and the role of primorials. * Explore the connection between prime gap distribution and other mathematical fields, such as cryptography and coding theory. By leveraging this dataset, researchers can gain a deeper understanding of the intricate patterns and underlying structures that govern the distribution of prime numbers.

Supplement to the Prime Gap Dataset Description Unveiling the Mysteries of Prime Gaps The Prime Gap Dataset offers a unique opportunity to delve into the fascinating world of prime numbers. By analyzing the distribution of gaps between consecutive primes, we can uncover hidden patterns and structures that might hold the key to unlocking the secrets of the universe. Key Features and Potential Insights: * Visual Exploration: Immerse yourself in stunning visualizations of prime gap distributions, revealing hidden patterns and anomalies. * Statistical Analysis: Conduct in-depth statistical analysis to identify trends, correlations, and outliers. * Machine Learning Applications: Employ machine learning techniques to predict prime gap distributions and discover novel insights. * Fractal Analysis: Investigate the potential fractal nature of prime number distributions, revealing self-similarity at different scales. Potential Research Directions: * Uncovering Hidden Patterns: Explore the distribution of prime gaps at various scales to identify emerging patterns and structures. * Predicting Prime Gap Behavior: Develop machine learning models to predict the size and distribution of future prime gaps. * Testing Mathematical Conjectures: Use the dataset to test conjectures related to prime number distribution, such as the Riemann Hypothesis. * Exploring Connections to Other Fields: Investigate the relationship between prime numbers and other mathematical fields, such as chaos theory and information theory. By delving into this rich dataset, you can contribute to the ongoing exploration of one of the most fundamental and enduring mysteries of mathematics.

We assess model performance using six datasets encompassing a broad taxonomic range. The number of species per dataset ranges from 28 to 239 (mean=118, median=94), and range shifts were observed over periods ranging from 20 to 100+ years. Each dataset was derived from previous evaluations of traits as range shift predictors and consists of a list of focal species, associated species-level traits, and a range shift metric.

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These results are from the rail demo of 5G-PICTURE (www.5g-picture-project.eu). For more details see Deliverable D6.3 where there are also plotted figures. Dataset 4-1 This dataset is generated by a computer model. The modulation and coding scheme (MCS) of a mmWave link between an access point (AP) and a station (STA) mounted on the roof of a train is plotted as a function of the distance between AP and STA. The IEEE 802.11ad single-carrier technology is assumed, and typical conditions when the range is approximately 350 m – in other words the lowest MCS, MCS1 can be supported up to this distance. The MCS takes integer values in range 1 to 12. Dataset 4-2 This dataset is generated by the same computer model as dataset 1. In this case we plot the predicted data rate (at the application layer in Gbps) and SNR (in dB). In the simulation we assume SNR requirements of an ideal AWGN channel and adjust the link budget to align with the typical range observed in the field. The SNR is also capped at a maximum value of 25dB commensurate with a real device. Datasets 4-5 to 4-12 This is a measured dataset from field testing of the Rail Demo. In the field test the train drives from one end of the test network to the other (over approximately 2km). Traffic (TCP iperf3) is generated within each trackside mmWave AP and sent to the train STAs when an association has been established. The datasets include measurement performed by the two STA of a single train node (TN), labelled ‘Train-1’. One STA has a radio facing forwards and one is facing backwards (see deliverable D6.3). These form the two datasets for each parameter. When a STA is not associated (i.e. has no mmWave link) the parameter is not recorded since no data packets are received. The following parameters are captured: Datasets 4-5 and 4-6 The modulation and coding scheme (MCS) of a mmWave link between an AP and each STA is logged. Datasets 4-7 and 4-8 The SNR is logged. SNR is measured in dB. Datasets 4-9 and 4-10 The sector ID here indicates which beam has been chosen by the TN radios when receiving packets. A STA maintains a beambook of 13 directional beams, and a beamforming protocol identify the best beam to use. The Sector ID is an integer from 1 to 13. Low beam numbers are close to boresight, whilst the highest numbers (up to 13) imply beam steering up to 45 degrees away from boresight. Odd numbers represent pointing to the left and even number point to the right. Datasets 4-11 and 4-12 This plots the received data rate by each STA at the application layer (TCP iperf3). Unit Mbps.

About this dataset

Context

The dataset tabulates the South Range population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of South Range across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of South Range was 741, a 0.27% decrease year-by-year from 2022. Previously, in 2022, South Range population was 743, an increase of 0.13% compared to a population of 742 in 2021. Over the last 20 plus years, between 2000 and 2023, population of South Range increased by 17. In this period, the peak population was 760 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the South Range is shown in this column.
• Year on Year Change: This column displays the change in South Range population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range Population by Year. You can refer the same here

This dataset provides information about the number of properties, residents, and average property values for Range View Circle cross streets in Silverthorne, CO.

About this dataset

Context

The dataset tabulates the population of South Range by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for South Range. The dataset can be utilized to understand the population distribution of South Range by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in South Range. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for South Range.

Key observations

Largest age group (population): Male # 20-24 years (49) | Female # 20-24 years (50). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the South Range population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the South Range is shown in the following column.
• Population (Female): The female population in the South Range is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in South Range for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the South Range, MI population pyramid, which represents the South Range population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for South Range, MI, is 21.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for South Range, MI, is 28.6.
• Total dependency ratio for South Range, MI is 50.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for South Range, MI is 3.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the South Range population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the South Range for the selected age group is shown in the following column.
• Population (Female): The female population in the South Range for the selected age group is shown in the following column.
• Total Population: The total population of the South Range for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range Population by Age. You can refer the same here