# Statistical Analyst

Part I I analyzed the compiled sample data set that was provided to me for the hypothesis test of the body fat and weight of 252 male members of SGG. I used excel to help me find the mean, median, range and standard deviation. The excel sample data set will is appended to this report. The average body fat of the SGG 252 male gym members I analyzed is 18.9 (SD = 7.8) and the average

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Part III

Scatter Plot with Regression Line (will submit excel file separate)

Identifying my independent / predictor variable for this analysis regression was important because it provided me with a logical explanation for the response variable. The predictor variable should precede the response variable in time or a causal relationship. Sometimes the predictor variable can be measured easily or have no casual force. This makes for a good way to estimate the response variable. In my regression analysis of body fat (y) vs. weight (x) I used weight as the predictor because it is measured easily. We can also use the weight measurements to estimate the body fat. I have concluded that there is a positive correlation between the weight (x) and body fat (y). The points on my scatter plot increase from left to right. As for what the correlation coefficient determines is if the strength and direction of the relation between the two variables, weight vs. body fat. The correlation between my two variables is 0.61315611. The linear on my scatter plot is a good fit for the sample data I was provided by SGG. The clusters are fairly tight around the line and have no obvious patterns to the differences; they do not fall into a curve. A slope shows how many percentage points of body fat are gained