Category: Statistics

I am doing an analysis on the mental health of an offender in Administrative Seg

I am doing an analysis on the mental health of an offender in Administrative Segregation while incarcerated. My data is showing me that administrative segregation DOES NOT decrease the mental health of inmates.
Here is my data:
https://www.icpsr.umich.edu/web/NACJD/studies/31321/summary
I need to use 10-14 independent variables however I must use 2 dependent variables. For my dependent variables I want to use anxiety and self-harm data.
I need to analyze my data with a
zero-order correlation and two types of
multivariate analysis: (1) Ordinary least-squares regression and (2) Logistic regression.
The zero-order correlation, ordinary least squares regression and logistic regression all have to go on Microsoft word on separate pages.
Attached I have an example on how the three tables need to be on Microsoft Word. This is an example and a paper on a completely different topic.
Thank you in advance.

Region: Start by picking one region from the following list of regions: West Sou

Region: Start by picking one region from the following list of regions:
West Sou

Region: Start by picking one region from the following list of regions:
West South Central, West North Central, East South Central, East North Central, Mid Atlantic
Purpose: What is the purpose of your analysis?
Sample: Define your sample. Take a random sample of 500 house sales for your region.
Describe what is included in your sample (i.e., states, region, years or months).
Questions and type of test: For your selected sample, define two hypothesis questions (see the Scenario above) and the appropriate type of test for each. Address the following for each hypothesis:
Describe the population parameter for the variable you are analyzing.
Describe your hypothesis in your own words.
Identify the hypothesis test you will use (1-Tail or 2-Tail).
Level of confidence: Discuss how you will use estimation and confidence intervals to help you solve the problem.
1-Tail Test
Hypothesis: Define your hypothesis.
Define the population parameter.
Write null (Ho) and alternative (Ha) hypotheses. Note: For means, define a hypothesis that is less than the population parameter.
Specify your significance level.
Data analysis: Summarize your sample data using appropriate graphical displays and summary statistics and confirm assumptions have not been violated to complete this hypothesis test.
Provide at least one histogram of your sample data.
In a table, provide summary statistics including sample size, mean, median, and standard deviation. Note: For quartiles 1 and 3, use the quartile function in Excel:
=QUARTILE([data range], [quartile number])
Summarize your sample data, describing the center, spread, and shape in comparison to the national information (under Supporting Materials, see the National Summary Statistics and Graphs House Listing Price by Region PDF). Note: For shape, think about the distribution: skewed or symmetric.
Check the conditions.
Determine if the normal condition has been met.
Determine if there are any other conditions that you should check and whether they have been met. Note: Think about the central limit theorem and sampling methods.
Hypothesis test calculations: Complete hypothesis test calculations.
Calculate the hypothesis statistics.
Determine the appropriate test statistic (t). Note: This calculation is (mean – target)/standard error. In this case, the mean is your regional mean, and the target is the national mean.
Calculate the probability (p value). Note: This calculation is done with the T.DIST function in Excel:
=T.DIST([test statistic], [degree of freedom], True) The degree of freedom is calculated by subtracting 1 from your sample size.
Interpretation: Interpret your hypothesis test results using the p value method to reject or not reject the null hypothesis.
Relate the p value and significance level.
Make the correct decision (reject or fail to reject).
Provide a conclusion in the context of your hypothesis.
2-Tail Test
Hypotheses: Define your hypothesis.
Define the population parameter.
Write null and alternative hypotheses. Note: For means, define a hypothesis that is not equal to the population parameter.
State your significance level.
Data analysis: Summarize your sample data using appropriate graphical displays and summary statistics and confirm assumptions have not been violated to complete this hypothesis test.
Provide at least one histogram of your sample data.
In a table, provide summary statistics including sample size, mean, median, and standard deviation. Note: For quartiles 1 and 3, use the quartile function in Excel:
=QUARTILE([data range], [quartile number])
Summarize your sample data, describing the center, spread, and shape in comparison to the national information. Note: For shape, think about the distribution: skewed or symmetric.
Check the assumptions.
Determine if the normal condition has been met.
Determine if there are any other conditions that should be checked on and whether they have been met. Note: Think about the central limit theorem and sampling methods.
Hypothesis test calculations: Complete hypothesis test calculations.
Calculate the hypothesis statistics.
Determine the appropriate test statistic (t). Note: This calculation is (mean – target)/standard error. In this case, the mean is your regional mean, and the target is the national mean.]
Determine the probability (p value). Note: This calculation is done with the TDIST.2T function in Excel:
=T.DIST.2T([test statistic], [degree of freedom]) The degree of freedom is calculated by subtracting 1 from your sample size.
Interpretation: Interpret your hypothesis test results using the p value method to reject or not reject the null hypothesis.
Compare the p value and significance level.
Make the correct decision (reject or fail to reject).
Provide a conclusion in the context of your hypothesis.
Comparison of the test results: Revisit Question 3 from the Scenario section: For your region, what is the range of values for the 95% confidence interval of square footage for homes?
Calculate and report the 95% confidence interval. Show or describe your method of calculation.
Final Conclusions
Summarize your findings: In one paragraph, summarize your findings in clear and concise plain language.
Discuss: Discuss whether you were surprised by the findings. Why or why not?
You can use the following tutorial that is specifically about this assignment:
MAT-240 Module 7 Project Two Video
What to Submit
To complete this project, you must submit the following:
Project Two Template Word Document Use this template to structure your report, and submit the finished version as a Word document.

Region: Start by picking one region from the following list of regions: West Sou

Region: Start by picking one region from the following list of regions:
West Sou

Region: Start by picking one region from the following list of regions:
West South Central, West North Central, East South Central, East North Central, Mid Atlantic
Purpose: What is the purpose of your analysis?
Sample: Define your sample. Take a random sample of 500 house sales for your region.
Describe what is included in your sample (i.e., states, region, years or months).
Questions and type of test: For your selected sample, define two hypothesis questions (see the Scenario above) and the appropriate type of test for each. Address the following for each hypothesis:
Describe the population parameter for the variable you are analyzing.
Describe your hypothesis in your own words.
Identify the hypothesis test you will use (1-Tail or 2-Tail).
Level of confidence: Discuss how you will use estimation and confidence intervals to help you solve the problem.
1-Tail Test
Hypothesis: Define your hypothesis.
Define the population parameter.
Write null (Ho) and alternative (Ha) hypotheses. Note: For means, define a hypothesis that is less than the population parameter.
Specify your significance level.
Data analysis: Summarize your sample data using appropriate graphical displays and summary statistics and confirm assumptions have not been violated to complete this hypothesis test.
Provide at least one histogram of your sample data.
In a table, provide summary statistics including sample size, mean, median, and standard deviation. Note: For quartiles 1 and 3, use the quartile function in Excel:
=QUARTILE([data range], [quartile number])
Summarize your sample data, describing the center, spread, and shape in comparison to the national information (under Supporting Materials, see the National Summary Statistics and Graphs House Listing Price by Region PDF). Note: For shape, think about the distribution: skewed or symmetric.
Check the conditions.
Determine if the normal condition has been met.
Determine if there are any other conditions that you should check and whether they have been met. Note: Think about the central limit theorem and sampling methods.
Hypothesis test calculations: Complete hypothesis test calculations.
Calculate the hypothesis statistics.
Determine the appropriate test statistic (t). Note: This calculation is (mean – target)/standard error. In this case, the mean is your regional mean, and the target is the national mean.
Calculate the probability (p value). Note: This calculation is done with the T.DIST function in Excel:
=T.DIST([test statistic], [degree of freedom], True) The degree of freedom is calculated by subtracting 1 from your sample size.
Interpretation: Interpret your hypothesis test results using the p value method to reject or not reject the null hypothesis.
Relate the p value and significance level.
Make the correct decision (reject or fail to reject).
Provide a conclusion in the context of your hypothesis.
2-Tail Test
Hypotheses: Define your hypothesis.
Define the population parameter.
Write null and alternative hypotheses. Note: For means, define a hypothesis that is not equal to the population parameter.
State your significance level.
Data analysis: Summarize your sample data using appropriate graphical displays and summary statistics and confirm assumptions have not been violated to complete this hypothesis test.
Provide at least one histogram of your sample data.
In a table, provide summary statistics including sample size, mean, median, and standard deviation. Note: For quartiles 1 and 3, use the quartile function in Excel:
=QUARTILE([data range], [quartile number])
Summarize your sample data, describing the center, spread, and shape in comparison to the national information. Note: For shape, think about the distribution: skewed or symmetric.
Check the assumptions.
Determine if the normal condition has been met.
Determine if there are any other conditions that should be checked on and whether they have been met. Note: Think about the central limit theorem and sampling methods.
Hypothesis test calculations: Complete hypothesis test calculations.
Calculate the hypothesis statistics.
Determine the appropriate test statistic (t). Note: This calculation is (mean – target)/standard error. In this case, the mean is your regional mean, and the target is the national mean.]
Determine the probability (p value). Note: This calculation is done with the TDIST.2T function in Excel:
=T.DIST.2T([test statistic], [degree of freedom]) The degree of freedom is calculated by subtracting 1 from your sample size.
Interpretation: Interpret your hypothesis test results using the p value method to reject or not reject the null hypothesis.
Compare the p value and significance level.
Make the correct decision (reject or fail to reject).
Provide a conclusion in the context of your hypothesis.
Comparison of the test results: Revisit Question 3 from the Scenario section: For your region, what is the range of values for the 95% confidence interval of square footage for homes?
Calculate and report the 95% confidence interval. Show or describe your method of calculation.
Final Conclusions
Summarize your findings: In one paragraph, summarize your findings in clear and concise plain language.
Discuss: Discuss whether you were surprised by the findings. Why or why not?
You can use the following tutorial that is specifically about this assignment:
MAT-240 Module 7 Project Two Video
What to Submit
To complete this project, you must submit the following:
Project Two Template Word Document Use this template to structure your report, and submit the finished version as a Word document.

Module 8 – Introduction This is the beginning of Unit 3. Unit 3 includes Modules

Module 8 – Introduction
This is the beginning of Unit 3. Unit 3 includes Modules

Module 8 – Introduction
This is the beginning of Unit 3. Unit 3 includes Modules 8, & 9. Unit 3 concludes with the Unit 3 Checkpoint.
Before we begin this Unit, let’s see how the new ideas in this module relate to the Big Picture of Statistics.
We begin a statistical investigation with a research question. The investigation proceeds with the following steps:
Produce Data: Determine what to measure, how to sample, then collect the data.
Explore the Data: Analyze and summarize the data.
Draw a Conclusion: Use the data, probability, and statistical inference to draw a conclusion about the population.
In the course up to this point, we have focused on summarizing and analyzing data for a quantitative variable. In this Unit, we focus on summarizing and analyzing the relationship between two quantitative variables. This material is still part of exploratory data analysis.
Introduction
Previously, we analyzed a quantitative variable using a graph (dotplot, histogram or boxplot) and a numerical summary (5-number summary or mean with standard deviation.)
We also examined the relationship between a categorical variable and a quantitative variable, by using the categorical variable to create two groups and analyzing the quantitative variable for each group. For example, we compared credit card debt (quantitative variable) for male and female college students (categorical variable.) For each student, we had two pieces of information: gender and credit card debt. In this situation we used side-by-side histograms or boxplots to compare the two groups.
In this Unit we examine the relationship between two quantitative variables. For each individual in the data set, we collect two pieces of quantitative information. For example, for a breakfast cereal, we could record sugar content in a serving and calories in a serving.
We use a graph called a scatterplot to graph the two pieces of information in two-dimensions.
In a scatterplot one variable is the explanatory variable, and the other is the response variable. The response variable is the focus of the study, the outcome that we examine. The explanatory variable is the variable that we think may influence the response. We can think of the explanatory variable as the predictor of the response.
EXAMPLE Scatterplot
A research firm conducts a study to explore the relationship between a driver’s age and the driver’s ability to read highway signs. The subjects are a random sample of 30 drivers between the ages of 18 and 82.
(Source: Jessica M. Utts and Robert F. Heckard, Mind on Statistics [Brooks/Cole, 2002]. Original source: Data collected by The Last Resource, Inc., Bellfonte, PA.)
Because the purpose of this study is to explore the effect of age on the driver’s ability to read highway signs,
the explanatory variable is age, and
the response variable is the maximum distance at which the driver can read a highway sign, or maximum reading distance.
Both variables are quantitative.
Here is what the raw data look like:
In this data set, the individuals are the 30 drivers. For each driver, we have two values: age and maximum reading distance.
To explore the relationship between age and distance, we create a graph called a scatterplot. To create a scatterplot, we use an ordered pair (x, y) to represent the data for each driver. The x-coordinate is the explanatory variable: driver’s age. The y-coordinate is the response variable: maximum reading distance.
For this example, the ordered pair (18, 510) represents an 18-year-old driver who can read a highway sign at a maximum distance of 510 feet. We plot a point for each ordered pair. In the scatterplot, each driver appears as a single point.
Generally, each point in a scatterplot represents one individual. However, a dot can represent more than one individual. For example, if two individuals have the same variable values, then one dot will represent them both.
The x-coordinate is the value of the explanatory variable for that individual. The y-coordinate is the value of the response variable for that individual.
Here is the completed scatterplot:

Comment
The explanatory variable is on the horizontal x-axis. The response variable is on the vertical y-axis. Sometimes the variables do not have a clear explanatory–response relationship. In this case, there is no rule to follow. Plot the variables on either axis.

Answer the four questions in details.. use excel sheet and word You can calcula

Answer the four questions in details.. use excel sheet and word
You can calcula

Answer the four questions in details.. use excel sheet and word
You can calculate
the p value using Excel. Suppose that you have a z-score Z. To calculate the two-tailed p-value, use the
following Excel command: =2*(1-NORM.S.DIST(ABS(Z), TRUE)). For example, you could
calculate the p-value of Z = -1.2 using =2*(1-NORM.S.DIST(ABS(-1.2), TRUE)).
Basis
1. State the null hypothesis (H0).
2. Set a significance level (α).
3. Calculate the estimate from the sample.
4. Define the sampling distribution and use it to calculate the p-value.
5. Reject or fail to reject H0 based on whether p-value < α