The activity includes two questions:
Some of the counties included in the dataset had reformed their welfare systems in the past ten years. An assumption of experimental design is that random assignment controls for differences between the groups. In this case, assume that the counties were randomly assigned to reform their welfare system or to maintain the current system.
Explore the effect of the welfare reform initiative using graphical displays. Compare the distribution of at least 3 variables that might be affected by welfare reform. You can use boxplots, histograms, etc. Do you see any effect of the welfare reform initiative on the variables that you selected?
Use the t-test (independent samples) to see if a county’s welfare system reform status influenced the unemployment rate.
Your report must include:
· For Question 1, you must include at least two graphical displays for each of the three variables you selected. The displays must compare the counties that implemented the welfare reform with the counties that did not implement the welfare reform.
· For Question 2, you must include the Minitab output and interpret the hypothesis testing results. What is the p-value? Do you reject or fail to reject? Interpret the results in the context of the problem.
· You must include at least three paragraphs for each question, in addition to Minitab output. Each paragraph must include at least four sentences.
Below are the graphical displays
Boxplot of Live Births
Histogram of Live Births
Boxplot of Number of Families in Poverty (
Histogram of Number of Families in Poverty (
Boxplot of Unemployment Rate (pct)
Histogram of Unemployment Rate (pct)
2-Sample t: Live Births by 2011-2013
2-Sample t: Number of Families in Poverty ( by 2011-2013
2-Sample t: Unemployment Rate (pct) by 2011-2013
Chapter 12 Minitab Express
The activity includes two questions. Using the data in the textbook dataset, answer the following
Some of the counties included in the dataset had reformed their welfare systems in the past ten
years. An assumption of experimental design is that random assignment controls for differences
between the groups. In this case, assume that the counties were randomly assigned to reform
their welfare system or to maintain the current system.
Explore the effect of the welfare reform initiative using graphical displays. Compare the
distribution of at least 3 variables that might be affected by welfare reform. You can use
boxplots, histograms, etc. Do you see any effect of the welfare reform initiative on the variables
that you selected?
For this question, you’ll use the variable Experimental Welfare Reform status in 2004 (C38) to identify the two groups. I will investigate the effect of the experimental welfare reform program on infant death rate (C60). I created boxplots (Graph, Boxplot, with groups, select C60, then Group variables for grouping C38).
Here is the output. Notice the summary statistics are shown below the boxplots.
We don’t see much difference in infant death rate between the counties with and without welfare reform. There is an outlier in the counties without welfare reform so I should investigate to identify this county and determine why there is an outlier. I can also create other displays (histograms, dotplots, etc).
Use the t-test (independent samples) to see if a county’s welfare system reform status
influenced the unemployment rate.
For this demo, I will investigate the effect of the welfare reform on the infant death rate. Use
Statistocs, Two-samples, t for the means.
Select “both samples are in one column,” select the column with the samples (C60) and the
column that identifies the groups (C38).
In options, select 95 confidence level, Alternative hypothesis: difference ≠ 0.00. Check to see
that “Assume equal variances” is not selected.
Here is my output:
We see that the t-statistic is -0.48 with a p-value of 0.6343. We fail to reject; we do not have
evidence that there is a difference in the infant death rate based on the welfare reform status of
the county. This is supported by the boxplot from question 1.
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