Appropriate question about relationship between variables posed.
I am interested in investigating if there is any (meaningful) relationship between the Girth of the chest in inches and the weight of the bear in pounds. I am using chest as my explanatory variable and weight as response variable. The data source is from Pennsylvania Game Commission. I will use the relationship (if any) to predict the weight for a chest girth of 60 inches.
Scatterplot drawn with the explanatory and response variables clearly identified.
Nature and strength of relationship described.
Visual aspects supported with ‘r’. Discussion and justification based on r only is not sufficient.
Correlation = 0.96
There appears to be a linear trend between the two variables. As the data points are relatively close to the regression line, it can be stated that there is strong positive association between chest and weight. The correlation coefficient of 0.96 (very close to 1) confirms that the linear relationship is very strong and positive.
Model obtained, equation specified in context and prediction made in context with sensible rounding (to nearest 0ne or ten)
NB: Do one Interpolation and one Extrapolation.
Model: WEIGHT = 12.54 * CHEST + -264.48.
The trend line equation is y = 12.544x - 264.48 which means that as the chest girth increases by 1 inch the weight of bear increases by 12.54 pounds. For a chest girth of 60 inches , the weight would be:
12.54 * 60 – 264.48 = 488 pounds
Conclusion written with reference to inquiry cycle (does not have to be specific and is implied via the investigation) Based on my analysis, it can be said that there is a very strong positive linear relationship (r = 0.96) between the chest and weight and that the chest girth is a good predictor of bear’s weight. Excel was used to produce the scatterplots on bears data and to obtain the trend line and its equation....
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