Section STAT 2480 Cancer Data Crossword Puzzle using the Minitab output generate

Question

Section STAT 2480 Cancer Data Crossword Puzzle using the Minitab output generated by the Cancer data from Lab 3, fill in the numerical answers for the following crossword puzzle. Periods will have their own box, and superfluous zeros are not included in the answers. For example, if you needed to enter 0.543", you would simply enter'·543" into four boxes. Mortality Exposure 2.49 2.57 3.41 1.25 1.62 3.83 11.64 6.41 8.34 130.1 129.9 113.5 137.5 162.3 207.5 County Umatilla Morrow Gilliam Sherman Wasco Hood River Portland Columbia Clatsop 210.3 Descriptive Statistics: Exposure, Mortality v ariable N Nt Msan SE Mear. StDcv Minimum Expasure 04.62 Mortality 9 153.3 Media3 ax i.um L.64 1.16 3.19 11.6 34.8 1.25 2.06 3.41 .38 113.5 130,0 147.1 192.? 21b.3 Correlations: Exposure, Nortality Pearson correlation of Exposure znd Mortality 0.926 P-Value-000 Regression Analysis: Mortality versus Exposure The regreasion equstion is Mortality 114.7+9.231 Exposure S 14,0099 R-Sq 85.8, R-Sq {adj , 83.8t - Analysia or variance DE MS Regression 1 309.56 6309,56 42.34 0.000 Error Total 1373.95 195.28 B 9683.50 What is the response variable? What is the predictor variable? Across 24% of variation in Mortality explained by Exposure 3. Exposure Standard Deviation 6. Mortality Mean 8. Correlation between Exposure & Mortality Down 1. Exposure Mean 4. Regression Equation Slope 5. Regression Equation Y-Intercept 7. Mortality Standard Deviation POTO

Explanation / Answer

Using the definition of the response variable and predictor variable:

The response variable is also known as the dependent variable. In other words, the response variable depends on another factor, known as the independent variable, to cause change, or response, to it.

A predictor variable is a variable that is being used to predict some other variable or outcome.

Thus, from the definition of the response variable and predictor variable we have

Response variable: Mortality

And Predictor variable: Exposure

Answers for the ACROSS:

2)

The coefficient of determination, r^2, is the proportion of the variation that explained by the regression line.

R-sq is the percentage of variation in the response that is explained by the model. The higher the R2value, the

better the model fits your data. r^2 is always between 0% and 100%.

Thus, the proportion of variation explained by the line is r^2= 0.858, so 85.8% is explained by the line.

3)

From the Descriptive Statistics we have

Exposure Standard Deviation = 3.49

6)

From the Descriptive Statistics we have

Mortality Mean = 157.3

8)

From the Minitab output we have Pearson correlation of Exposure and Mortality is 0.926.

That is correlation between Exposure and Mortality = 0.926

Answers for the DOWN:

1)

From the Descriptive Statistics we have

Exposure Mean = 4.62

4)

The equation for a straight line " y = a + bx " which summarises the relationship between them. Here y is the outcome, response or dependent variable (output), while x is the predictor, explanatory or independent variable (input).

Slope and intercept of the regression line. The slope indicates the steepness of a line and the intercept indicates the location where it intersects an axis. The slope and the intercept define the linear relationship between two variables, and can be used to estimate an average rate of change.

This relationship can be represented by the equation

                 y = a + bx,

Where a is the y-intercept and b is the slope.

We have the regression equation is

Mortality (y) = 114.7 + 9.231 Exposure (x)

Thus, the regression Equation Slope = 9.231

5)

Regression equation Y – intercept = 114.7

7)

From the Descriptive Statistics we have

Mortality Standard Deviation = 34.8

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