Problem Statement

You are the CEO of a restaurant franchise and are considering different cities for opening a new outlet.

You would like to expand your business to cities that may give your restaurant higher profits. The chain already has restaurants in various cities and you have data for profits and populations from the cities. You also have data on cities that are candidates for a new restaurant. For these cities, we have the city population. Task is to use the data to help you identify which cities may potentially give the business higher profits?

Dataset

x_train is the population of a city y_train is the profit of a restaurant in that city. A negative value for profit indicates a loss. Both X_train and y_train are numpy arrays.

Evaluating the status of threatened and endangered salmonid populations requires information on the current status of the threats (e.g., habitat, hatcheries, hydropower, and invasives) and the risk of extinction (e.g., status and trend in the Viable Salmonid Population criteria). For salmonids in the Pacific Northwest, threats generally result in changes to physical and biological characteristics of freshwater habitat. These changes are often described by terms like "limiting factors" or "habitat impairment." For example, the condition of freshwater habitat directly impacts salmonid abundance and population spatial structure by affecting carrying capacity and the variability and accessibility of rearing and spawning areas. Thus, one way to assess or quantify threats to ESUs and populations is to evaluate whether the ecological conditions on which fish depend is improving, becoming more degraded, or remains unchanged. In the attached spreadsheets, we have attempted to consistently record limiting factors and threats across all populations and ESUs to enable comparison to other datasets (e.g., restoration projects) in a consistent way. Limiting factors and threats (LF/T) identified in salmon recovery plans were translated in a common language using an ecological concerns data dictionary (see "Ecological Concerns" tab in the attached spreadsheets) (a data dictionaries defines the wording, meaning and scope of categories). The ecological concerns data dictionary defines how different elements are related, such as the relationships between threats, ecological concerns and life history stages. The data dictionary includes categories for ecological dynamics and population level effects such as "reduced genetic fitness" and "behavioral changes." The data dictionary categories are meant to encompass the ecological conditions that directly impact salmonids and can be addressed directly or indirectly by management (habitat restoration, hatchery reform, etc.) actions. Using the ecological concerns data dictionary enables us to more fully capture the range of effects of hydro, hatchery, and invasive threats as well as habitat threat categories. The organization and format of the data dictionary was also chosen so the information we record can be easily related to datasets we already posses (e.g., restoration data). Data Dictionary.

Use this summary report to properly interpret 2020 NSDUH estimates of substance use and mental health issues. The report accompanies theannual detailed tablesand covers overall methodology, key definitions for measures and terms used in 2020 NSDUH reports and tables, and selected analyses of the measures and how they should be interpreted.The report is organized into six chapters:Introduction.Description of the survey, including information about the sample design, data collection procedures, and key aspects of data processing such as development of the analysis weights.Technical details on the statistical methods and measurement, such as suppression criteria for unreliable estimates, statistical testing procedures, issues around data accuracy, and measurement issues for selected substance use and mental health measures.Special topics related to prescription psychotherapeutic drugs.A comparison between NSDUH and other sources of data on substance use and mental health issues, including data sources for populations outside the NSDUH target population.A more in-depth view of special methodological issues for the 2020 NSDUH, including those related to methodological changes made because of the Coronavirus 2019 Pandemic.An appendix covers key definitions used in NSDUH reports and tables.

Use this summary report to properly interpret 2021 NSDUH estimates of substance use and mental health issues. The report accompanies theannual detailed tablesand covers overall methodology, key definitions for measures and terms used in 2021 NSDUH reports and tables, and selected analyses of the measures and how they should be interpreted.The report is organized into six chapters:Introduction.Description of the survey, including information about the sample design, data collection procedures, and key aspects of data processing such as development of the analysis weights. The report also includes methodological changes and related issues in the 2021 NSDUH due to COVID-19.Technical details on the statistical methods and measurement, such as suppression criteria for unreliable estimates, statistical testing procedures, issues around selected substance use and mental health measures, and the impact of methodological changes on response rates.Special topics related to prescription psychotherapeutic drugs.A comparison between NSDUH and other sources of data on substance use and mental health issues, including data sources for populations outside the NSDUH target population.A more in-depth view of special methodological issues for the 2021 NSDUH, including the results of special analyses that led SAMHSA to not compare estimates from 2021 to estimates from previous years.An appendix covers key definitions used in NSDUH reports and tables.

This report summarizes methods and other supporting information that are relevant to estimates of substance use and mental health issues from the 2014 National Survey on Drug Use and Health (NSDUH), an annual survey of the civilian, noninstitutionalized population of the United States aged 12 years old or older. This report is organized into six sections. Section A describes the survey, including information about the sample design, data collection procedures, and key aspects of data processing (e.g., development of analysis weights). Section B presents technical details on the statistical methods and measurement, such as suppression criteria for unreliable estimates, statistical testing procedures, and issues for selected substance use and mental health measures. A glossary that covers key definitions used in NSDUH reports and tables is included in Section C. Section D describes other sources of data on substance use and mental health issues, including data sources for populations outside the NSDUH target population. A list of references cited in the report (Section E) and contributors to this report (Section F) also are provided.

Use this summary report to properly interpret 2019 NSDUH estimates of substance use and mental health issues. The report accompanies theannual detailed tablesand covers overall methodology, key definitions for measures and terms used in 2019 NSDUH reports and tables, and selected analyses of the measures and how they should be interpreted.The report is organized into five chapters:Introduction.Description of the survey, including information about the sample design, data collection procedures, and key aspects of data processing such as development of the analysis weights.Technical details on the statistical methods and measurement, such as suppression criteria for unreliable estimates, statistical testing procedures, issues around data accuracy, and measurement issues for selected substance use and mental health measures.Special topics related to prescription psychotherapeutic drugs.A comparison between NSDUH and other sources of data on substance use and mental health issues, including data sources for populations outside the NSDUH target population.An appendix covers key definitions used in NSDUH reports and tables.

Abstract

The primary objective of the 2018 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence of malaria, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), disability, and other health-related issues such as smoking.

The information collected through the 2018 NDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The 2018 NDHS also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nigeria.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-5 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 NDHS is the Population and Housing Census of the Federal Republic of Nigeria (NPHC), which was conducted in 2006 by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into wards. In addition to these administrative units, during the 2006 NPHC each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2018 NDHS, is defined on the basis of EAs from the 2006 EA census frame. Although the 2006 NPHC did not provide the number of households and population for each EA, population estimates were published for 774 LGAs. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census was used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rural for the survey sample frame. Before sample selection, all localities were classified separately into urban and rural areas based on predetermined minimum sizes of urban areas (cut-off points); consistent with the official definition in 2017, any locality with more than a minimum population size of 20,000 was classified as urban.

The sample for the 2018 NDHS was a stratified sample selected in two stages. Stratification was achieved by separating each of the 36 states and the Federal Capital Territory into urban and rural areas. In total, 74 sampling strata were identified. Samples were selected independently in every stratum via a two-stage selection. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using a probability proportional to size selection during the first sampling stage.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2018 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

The processing of the 2018 NDHS data began almost immediately after the fieldwork started. As data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding the open-ended questions. The NPC data processor coordinated the exercise at the central office. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed in the second week of April 2019.

Response rate

A total of 41,668 households were selected for the sample, of which 40,666 were occupied. Of the occupied households, 40,427 were successfully interviewed, yielding a response rate of 99%. In the households interviewed, 42,121 women age 15-49 were identified for individual interviews; interviews were completed with 41,821 women, yielding a response rate of 99%. In the subsample of households selected for the male survey, 13,422 men age 15-59 were identified and 13,311 were successfully interviewed, yielding a response rate of 99%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2018 Nigeria Demographic and Health Survey (NDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2018 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2018 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Standardisation exercise results from anthropometry training - Height and weight data completeness and quality for children - Height measurements from random subsample of measured children - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends - Data collection period - Malaria prevalence according to rapid diagnostic test (RDT)

Note: See detailed data quality tables in APPENDIX C of the report.

This report summarizes the 2018 NSDUH methods and other supporting information relevant to estimates of substance use and mental health issues, and organized into five chapters. Chapter 1 is an introduction to the report. Chapter 2 describes the survey, including information about the sample design; data collection procedures; and key aspects of data processing, such as development of analysis weights. Chapter 3 presents technical details on the statistical methods and measurement, such as suppression criteria for unreliable estimates, statistical testing procedures, and issues for selected substance use and mental health measures. Chapter 4 covers special topics related to prescription psychotherapeutic drugs. Chapter 5 describes other sources of data on substance use and mental health issues, including data sources for populations outside the NSDUH target population. Appendix A is a glossary that covers key definitions for use as a resource with the 2018 NSDUH reports and detailed tables. Appendix B provides a list of contributors to the report.