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
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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:
 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:

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 ttest will be used. This is chosen because we have only two groups that are heterogenous Assumptions

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 pvalue:
Mean (not married)=18485.67; mean (married)=25315.53; mean difference=6829.87 Teststatistic=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 Teststatistic=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