a. List the statistical procedures used to describe the sample.
b. Was the level of significance or alpha identified? If so, indicate the level (.05, .01, or .001).
c. Complete the table below with the analysis techniques conducted in the study:
1) Identify the purpose (description, relationships, or differences) of each analysis technique.
2) List the statistical procedures.
3) List the statistics.
4) Identify specific results.
5) Provide specific probability value (p =)
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Note: This article has very few statistical procedures located (there is only one noted in the results). This was a quantitative study.
a. The sample was randomized. 52 physicians were split into two groups to assess hand washing techniques based on the location of an alcohol based hand rub dispenser.
b. alpha was not identified.
c. Only one stat identified: ...
The statistical procedures used to describe the samples are listed. The level of significance or alpha identified are determined.
Statistics Problems - Regression Analysis, Autocorrelation, Multicollinearity
1. Suppose an appliance manufacturer is doing a regression analysis, using quarterly time-series data, of the factors affecting its sales of appliances. A regression equation was estimated between appliance sales (in dollars) as the dependent variable and disposable personal income and new housing starts as the independent variables. The statistical tests of the model showed large t-values for both independent variables, along with a high r2 value. However, analysis of the residuals indicated that substantial autocorrelation was present.
a. What are some of the possible causes of this autocorrelation?
b. How does this autocorrelation affect the conclusions concerning the significance of the individual explanatory variables and the overall explanatory power of the regression model?
c. Given that a person uses the model for forecasting future appliance sales, how does this autocorrelation affect the accuracy of these forecasts?
d. What techniques might be used to remove this autocorrelation from the model?
2. Suppose the appliance manufacturer discussed in Exercise 1 also developed another model, again using time-series data, where appliance sales was the dependent variable and disposable personal income and retail sales of durable goods were the independent variables. Although the r2 statistic is high, the manufacturer also suspects that serious multicollinearity exists between the two independent variables.
a. In what ways does the presence of this multicollinearity affect the results of the regression analysis?
b. Under what conditions might the presence of multicollinearity cause problems in the use of this regression equation in designing a marketing plan for appliance sales?View Full Posting Details