just answers these question based on the project make the answers short as can as you could QUALITATIVE. These are intentionally open-ended. In "real life" an interviewer would be mostly looking

I. Histograms

For each variable, provide a histogram that summarizes its distribution. Make sure each histogram has a title and that both chart axes have labels.

just answers these question based on the project make the answers short as can as you could QUALITATIVE. These are intentionally open-ended. In "real life" an interviewer would be mostly looking 1

just answers these question based on the project make the answers short as can as you could QUALITATIVE. These are intentionally open-ended. In "real life" an interviewer would be mostly looking 2

just answers these question based on the project make the answers short as can as you could QUALITATIVE. These are intentionally open-ended. In "real life" an interviewer would be mostly looking 3

just answers these question based on the project make the answers short as can as you could QUALITATIVE. These are intentionally open-ended. In "real life" an interviewer would be mostly looking 4

II. Simple Regression Analysis

In this section, you will report results from performing SIMPLE regression with each of your independent variables.
Estimated Regression Parameters and R-squared. Please report the regression parameters and R-squared values found via simple regression. Provide UNITS with your regression coefficients.

Independent Variable Name	R-squared	(y-intercept)	(slope)
State Majority (after 2012 elections)	0.616858	0.222222	0.777778
Clinton votes percentage	0.540414	1.924159	-0.02999
Electoral College Votes	0.001831	0.602996	-0.00218

Residual Plots. For each simple regression model, provide a residual plot showing e (the residual error) versus x. Make sure each plot has a title and that both axes have labels.

Inference. Populate the following table using a confidence level of 99%.

Independent Variable Name	p-value for	p-value for	Is relationship significant at 99% confidence level? (Y/N)	Confidence Interval (Lower, Upper)		Does inference appear to be valid? (Y/N)
State Majority (after 2012 elections)	0.000549	1.44E-11	Yes	0.540469	1.015087	Yes
Clinton (%age of votes won)	7.3E-14	1.21E-09	Yes	-0.0407	-0.01928	Yes
Electoral College Votes	6.45E-07	0.767965	No	-0.02186	0.017508	Yes

III. Multiple Regression Analysis

Next, you will perform a MULTIPLE regression using all of the independent variables together.

Correlation Table. Provide a table showing the correlation between your independent variables. This is important for knowing whether your model might suffer from multi-collinearity. Use a different cell color to indicate values that are greater than 0.5 in absolute value (positive or negative).

	State Majority (after 2012 elections)	Clinton	Electoral College Votes
State Majority (after 2012 elections)		-0.7468	-0.19103
Clinton (%age of votes won)	-0.7468		0.25983
Electoral College Votes	-0.19103	0.25983

Estimated Regression Parameters and Confidence Intervals. Report the regression parameters found via multiple regression using the table below. Provide UNITS with your regression coefficients.

Parameter	Estimated Value	p-value	Significant at 99% confidence? (Y/N)	Confidence Interval (Lower, Upper)
y-intercept	0.947624238	0.001043	Yes	0.220267561	1.674980915
State Majority (after 2012 elections)	0.527877739	0.000072912	Yes	0.202426659	0.853328818
Clinton (%age of votes won)	-0.015500733	0.003726174	Yes	-0.029129389	-0.001872077
Electoral College Votes	0.007982467	0.070869747	No	-0.003616981	0.019581915

Residual Plot. Provide a SINGLE residual plot showing e (the residual error) versus . Make sure each plot has a title and that each plot axis has a label.

Multiple Regression Summary. Answer the following questions:

What is the Adjusted R-squared value for this model?

R2 = 0.691269241

Does inference appear to be valid based on your residual plot?

Yes