# Hypothesis Testing Assignment Help

## Hypothesis Testing Assignment Help

Hypothesis testing is used to infer a result of a hypothesis performed on sample data from a larger population. For example, performing a hypothesis test on sample data in an attempt to determine the mean of a population is the same as the mean of the sample.

Hypothesis Testing deals with basic concepts in statistics such as Parametric Statistics, Non parametric tests, Confidence intervals, Significance of test, Null Hypothesis, Alternate Hypothesis etc. Though these concepts are basic and lay the foundation of students in statistics, they can be complex at times. Our talented pool of Statistics experts, Statistics assignment tutors and Statistics homework tutors can cater to your entire needs in the area of Hypothesis Testing such as Hypothesis Testing Homework Help, Assignment Help, Project Paper Help and Exam Preparation Help. Our Statistics Tutors panel consists of talented and highly experienced Hypothesis Testing Solvers and Hypothesis Testing Helpers who are available 24/7 to provide you with high quality Undergraduate Statistics Assignment Help and Graduate Statistics Assignment Help. Along with College Statistics Homework Help and University Statistics Homework Help we also provide Online Hypothesis Testing tutoring for high school, undergraduate, graduate and PhD level students

Following is the comprehensive list of topics in which we offer the quality solutions:

• Decision Rulet-test
• Power of a Test
• Type II Error
• Statistical Hypothesis Testing
• Sampling Theory
• Types of Error
• Testing a Proportion
• Parametric Statistics
• ANOVA
• One-Sided Tests
• Hypothesis Formulation
• Relationship Between a and ß
• Testing a Mean: Known Population Variance
• Kruskal-Wallis Test
• Mann-Whitney U Test
• Testing a Mean: Unknown Population
• Z-test
• Consequences of Type II Error
• Type I Error
• Tests for One Variance
• Power Curves and OC Curves
• Testing of Hypothesis
• Confidence Intervals
• Z-tests, T-tests, Chi-square tests
• Probability
• Power and Confidence Intervals
• Non-parametric statistics
• One-way MANOVA
• Paired-samples t-test
• Wilcoxon Signed Rank Test

HYPOTHESIS TESTING

The question asked  in this set can be segregated into two parts. In the first part the examiner wants  students to develop an Inferential statistics assignment plan and also to execute the plan.  In the second part the examiner wants the students to write up the results of findings after the analysis. Statisticsassignmentexperts.com is the best site wherein the students can get help with various statistics topics at an affordable price.

SOLUTION

Part A

Introduction:

Variables Selected:

Table 1: Variables Selected for Analysis

 Variable Name in the Data Set Variable Type Description Qualitative or Quantitative Variable 1: Marital status Marital Status of Head of Household Qualitative Variable 2: Housing Total Amount of Annual Expenditure on Housing Quantitative Variable 3: Electricity Total Amount of Annual Expenditure on Electricity Quantitative

Data Analysis:

1. Confidence Interval Analysis: For one expenditure variable, select and run the appropriate method for estimating a parameter, based on a statistic (i.e., confidence interval method) and complete the following table (Note: Format follows Kozak outline):

Table 2: Confidence Interval Information and Results

 Name of Variable:  Electricity State the Random Variable and Parameter in Words: Total Amount of Annual Expenditure on Electricity, the parameter is the mean Confidence interval method including confidence level and rationale for using it: The confidence level is 0.95. the rationale is to estimate a range of value which is likely to contain the population parameter which is the population mean. State and check the assumptions for confidence interval: Central limit theory must be met The data must be randomly sampled. The sample values must be independent of another The sample size n should be no more than 10% of the population The sample size must be sufficiently large Method Used to Analyze Data: IBM SPSS Find the sample statistic and the confidence interval: The mean of electricity expenses is \$1431.23 while the confidence interval is [1399.96,1462.51] Statistical Interpretation: The average amount spent by household on electricity is \$1,431.23. We expect the true average money spent on electricity for the whole population to fall between \$1,399.96 and \$1,462.51 with 95% confidence.

2. Hypothesis Testing: Using the second expenditure variable (with socioeconomic variable as the grouping variable for making two groups), select and run the appropriate method for making decisions about two parameters relative to observed statistics (i.e., two sample hypothesis testing method) and complete the following table (Note: Format follows Kozak outline):

Table 3: Two Sample Hypothesis Test Analysis

 Research Question: Is there an significant difference in the average amount spent on housing  by married and unmarried head of household Two Sample Hypothesis Test that Will Be Used and Rationale for Using It: Independent samples t-test will be used. This is chosen because we have only two groups that are heterogenous Assumptions The dependent variable is continuous while the independent variable is categorical There dependent variable is normally distributed There is homogeneity of variance between the two groups There is no significant outliers State the Random Variable and Parameters in Words: The variable is the housing expenditure in USS, the parameter of interest is the mean of the two groups. State Null and Alternative Hypotheses and Level of Significance: Null hypothesis: the average amount spent on housing by unmarried head of household is the same as married head of household Alternative hypothesis: the average amount spent on housing by unmarried head of household is significantly different from that of married head of household The level of  significance is 5% Method Used to Analyze Data: IBM SPSS Find the sample statistic, test statistic, and p-value: Mean (not married)=18485.67; mean (married)=25315.53; mean difference=-6829.87 Test-statistic=-12.934, p=0.0000 Conclusion Regarding Whether or Not to Reject the Null Hypothesis: T(28)=-12.934, p=0.000<0.05. therefore, we reject the null hypothesis and conclude that the average amount spent on housing by unmarried head of household is significantly different from that of married head of household.

Part B: Results Write Up

Confidence Interval Analysis:

The mean of electricity expenses is \$1431.23 while the confidence interval is [1399.96, 1462.51]

Two Sample Hypothesis Test Analysis:

Mean (not married)=18485.67; mean (married)=25315.53; mean difference=-6829.87 Test-statistic=-12.934, p=0.0000

Discussion:

The average amount spent on electricity by our sample is \$1,431.23. however, we are 95% confident that the population average of amount spent on electricity will be between 1,399.96 and 1,462.51.

Moreover, we compare the housing expenses of head of families that are not married and those that are married. We found a significant difference between the average housing expenditure of married head of household (M=25,315.53) and unmarried head of household (M=18,485.67), t(28)=-12.934,p=0.000<0.05