This scatterplot is graphing the quantitative variables of weight and length.
No outliers should be removed.
My pvalue should be very close to 0.
My line of best fit will have a slope of 0.0024 and a standard deviation of 3.5.
Simple linear regression results:
Dependent Variable: Length in cm Independent Variable: Weight in Grams Length in cm = 42.401967 + 0.0023777771 Weight in Grams Sample size: 57 R (correlation coefficient) = 0.48952036 Rsq = 0.23963018 Estimate of error standard deviation: 3.5415865 Parameter estimates:
Analysis of variance table for regression model:

My correlation coefficient is 0.48952036, therefore, there is a moderate positive relationship.
Yes, my terms are significant.
My line of best fit has an intercept of 42.4 and a slope of 0.00237.
My Rsq is 0.2396 which expressed how well the regression line approximates the data points.
No, it is not a good fit for my data because an Rsq of 1 would mean that the line perfectly represents the data and my Rsq is 0.23 which isn't even close to being perfect.
No, my data is not correlated.
Yes, my expected values follow a normal distribution and appear to be leftskewed.
Yes, my residual plot implies that the linear model is a good fit.
No, the results were the same.
Yes, it is different than the model for my whole data set because there was less data that was more alike and therefore made the line of best fit almost perfect.
Multiple linear regression results:
Dependent Variable: Weight in Grams Independent Variable(s): Gestation Period (Weeks), Length in cm Weight in Grams = 7532.7019 + 194.34399 Gestation Period (Weeks) + 68.951014 Length in cm Parameter estimates:
Analysis of variance table for multiple regression model:
Summary of fit: Root MSE: 553.25463 Rsquared: 0.5702 Rsquared (adjusted): 0.5542 
This data now represents all my quantitative data. It includes the gestation period which hasn't been included in this part of the project. This changed my Rsq.
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Nov 18, 2017
The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. Usually it is 0.05 or 0.01.
When determine whether a slope is significant or not, we compare the Pvalue with the significance level
R^2 is the percentage of variance of response explained by linear model