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# Biostatistics Problems

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I am not sure what to do and these are very hard. Please help me as I can use this as an tutorial. Attached is also the data information

6.56 Obtain descriptive statistics and graphic displays for these salt-taste indices. Do the indices appear to be normally distributed? Why or why not? Compute the sample mean for this index, and obtain 95% CIs about the point estimate.

6.57 Construct indices measuring responsiveness to sugar taste, and provide descriptive statistics and graphical displays for these indices. Do the indices appear normally distributed? Why or why not? Compute the sample mean and associated 95% CIs for these indices.

6.58 We want to relate the indices to blood-pressure level. Provide a scatter plot relating mean SBP and mean DBP,respectively, to each of the salt-taste and sugar-taste indices. Does there appear to be a relation between the indices and blood-pressure level?

https://brainmass.com/statistics/descriptive-statistics/biostatistics-problems-525866

#### Solution Preview

6.56 Obtain descriptive statistics and graphic displays forthese salt-taste indices. Do the indices appear to be normally distributed? Why or why not? Compute the sample mean for this index, and obtain 95% CIs about the point estimate.
After organizing the data for salt taste indices, we could run "descriptive statistics" under "data analysis" in excel and obtain the following output:
Column1

Mean -2.7623
Standard Error 0.737648
Median -2.5
Mode 0
Standard Deviation 7.376478
Sample Variance 54.41243
Kurtosis 1.815535
Skewness 0.034726
Range 48
Minimum -22.75
Maximum 25.25
Sum -276.23
Count 100

For the data set, we could arrange them into 10 classes into the following ...

#### Solution Summary

The expert constructs the indices measuring responsiveness to sugar taste.

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## 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?

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