In 2022, when asked about tech skills gaps in their company, 27 percent of respondents reported that the main gaps in tech skills today were IT technicians. Looking to the future, IT decision-makers anticipated that the main gaps in tech skills were going to remain similar to those seen today, with AI/machine learning topping the list.

R Code for Analyses. This is a zip file containing all of the R code used to perform simulations and to analyze the breast cancer data. (ZIP 407 kb)

This statistic shows The Gap, Inc.'s comparable store sales growth worldwide from 2015 to 2024. In 2024, The Gap Inc.'s comparable store sales increased by approximately one percent.

Determining intrinsic number of clusters in a multidimensional dataset is a commonly encountered problem in exploratory data analysis. Unsupervised clustering algorithms often rely on specification of cluster number as an input parameter. However, this is typically not known a priori. Many methods have been proposed to estimate cluster number, including statistical and information-theoretic approaches such as the gap statistic, but these methods are not always reliable when applied to non-normally distributed datasets containing outliers or noise. In this study, I propose a novel method called hierarchical linkage regression, which uses regression to estimate the intrinsic number of clusters in a multidimensional dataset. The method operates on the hypothesis that the organization of data into clusters can be inferred from the hierarchy generated by partitioning the dataset, and therefore does not directly depend on the specific values of the data or their distribution, but on their relative ranking within the partitioned set. Moreover, the technique does not require empirical data to train on, but can use synthetic data generated from random distributions to fit regression coefficients. The trained hierarchical linkage regression model is able to infer cluster number in test datasets of varying complexity and differing distributions, for image, text and numeric data, using the same regression model without retraining. The method performs favourably against other cluster number estimation techniques, and is also robust to parameter changes, as demonstrated by sensitivity analysis. The apparent robustness and generalizability of hierarchical linkage regression make it a promising tool for unsupervised exploratory data analysis and discovery.

In 2023, building internal capabilities was the biggest talent strategy that was implemented to address the skill gap, with a ** percent share of survey respondents reporting the same. This was followed by hiring more generalists and focusing on adaptability with a ** percent share. Only ** percent of respondents reported outsourcing technical skills to address skills gap within their organizations.

About this dataset

Context

The dataset tabulates the population of Judith Gap by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Judith Gap across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 64.89% of total population being male. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Judith Gap is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Judith Gap total population. 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 Judith Gap Population by Race & Ethnicity. You can refer the same here

Financial overview and grant giving statistics of Gap International

About this dataset

Context

The dataset tabulates the population of Wind Gap by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Wind Gap. The dataset can be utilized to understand the population distribution of Wind Gap by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Wind Gap. 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 Wind Gap.

Key observations

Largest age group (population): Male # 55-59 years (167) | Female # 55-59 years (212). 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 Wind Gap population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Wind Gap is shown in the following column.
• Population (Female): The female population in the Wind Gap 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 Wind Gap 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 Wind Gap Population by Gender. You can refer the same here

Age Gap of Partner Within Family Units 2011 to 2016 by Type of Family Unit, Age of Husband, CensusYear and Statistic

View data using web pages

Download .px file (Software required)

Financial overview and grant giving statistics of Gap Group Inc

Ethnicity pay gap estimates for 2018 across different ethnicity breakdowns using the Annual Population Survey.

In 2024, the apparel retailer Gap, Inc. had net sales amounting to about 15.09 billion U.S. dollars. This represents a slight increase from the 14.9 billion dollars in the previous year. In 2022, the company had cited inventory delays due to global supply chain disruptions as the primary reason for the fall in net sales, as well as strategic store closures. The fiscal year end of the company is February 1, 2025. The Gap, Inc. The Gap, Inc. is an American clothing and accessories retailer based in San Francisco, California and was founded in 1969 by Donald and Doris Fisher. The Gap is a major international clothing retailer and brand. The Gap, Inc. also owns and operates the Old Navy, Banana Republic, Athleta, and Intermix brands. In 2024, The Gap, Inc. operated a total of 3,569 stores. The majority of the company’s stores are in North America, with 453 Gap stores throughout the region as of 2024. Leading Apparel Companies in the United States In terms of sales, the leading American apparel company is TJX Companies, which owns brands such as TJ Maxx, Marshalls and HomeGoods. However, when it comes to consumer favorites, the brand Levi's was the clothing brand viewed most favourably by consumers in the U.S. in 2024.

Financial overview and grant giving statistics of Standing in the Gap

Financial overview and grant giving statistics of Filling The Gap Inc

1. Compile a commercial gap table for the price difference between the wholesale and purchase prices for distributors at all levels, and deduct the remaining portion after deducting the shipping cost; it is also the production value created by distributors at all levels engaged in the buying and selling of goods. 2. Collection purpose: demonstration of the industry structure and the interdependence between industry sectors. 3. Data collection method: mainly referring to the industrial and service censuses conducted every five years and various related statistical data.

About this dataset

Context

The dataset tabulates the population of Mortons Gap by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Mortons Gap. The dataset can be utilized to understand the population distribution of Mortons Gap by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Mortons Gap. 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 Mortons Gap.

Key observations

Largest age group (population): Male # 55-59 years (50) | Female # 55-59 years (87). 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 Mortons Gap population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Mortons Gap is shown in the following column.
• Population (Female): The female population in the Mortons Gap 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 Mortons Gap 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 Mortons Gap Population by Gender. You can refer the same here

The gap is a measurement of the difference between the error within a group and its expected value under a reference (null) distribution. Sk is the standard deviation of the log of distance vectors of the reference data for k clusters Gap(k). The value of k is chosen as the smallest k where Gap(k) ≥ Gap(k+1)−Sk+1 and is shown in bold.

About this dataset

Context

The dataset tabulates the population of Union Gap by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Union Gap. The dataset can be utilized to understand the population distribution of Union Gap by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Union Gap. 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 Union Gap.

Key observations

Largest age group (population): Male # 20-24 years (385) | Female # 15-19 years (505). 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 Union Gap population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Union Gap is shown in the following column.
• Population (Female): The female population in the Union Gap 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 Union Gap 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 Union Gap Population by Gender. You can refer the same here