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Regression Part 2 (SLR Part 1) of 3 Parts

Regression Part 2 (SLR Part 1) of 3 Parts. Simple Linear Regression Overview Variation in Regression Hypothesis Testing Assumptions Homework Next up Multiple Regression Use of dummy variables. Simple Linear Regression Overview / Review.

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Regression Part 2 (SLR Part 1) of 3 Parts

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  1. Regression Part 2 (SLR Part 1) of 3 Parts • Simple Linear Regression Overview • Variation in Regression • Hypothesis Testing • Assumptions • Homework • Next up • Multiple Regression • Use of dummy variables

  2. Simple Linear Regression Overview / Review • Study of how one variable is related or associated with another. • Do larger stores generate greater sales. • In football how are total yards associated with scores. • How are advertising expenditures and sales related. • How are support budget and customer satisfaction associated. • One independent variable and one dependent variable. • Based on the equation for a line. • Predict or estimate the value on one variable based on another.

  3. n=21 pairs of observations/data

  4. SLR Manual Calculations SSX SSY Syx F*= MSR/MSE

  5. Assessing the Model t-test t*=17.2525 df=n-2 F-test F*=297.65 dfnum=1 dfdenom=n-2 With a very high level of confidence, we can say that temperature is significantly related to sales. Temperature does a good job in estimating sales.

  6. ANOVA Table for SLR

  7. Assumptions • There are certain assumptions that must be met or need to be near met for our method of least squares and results to be valid. e~iid(0,s2) • LINE • L – Linearity • I – Independence of errors • N – Normality • E – Equal Variance

  8. Homework (#4) • 13.28 Using cubic feet to estimate labor hours (2 variables only) • Try to obtain SSR, SSE, SST, and F* manually in Excel. • Run the t and F tests and interpret these results. • Make sure you can obtain and understand the SLR ANOVA table in NCSS. • Perform a basic set of assumptions by interpreting a scatter plot and residual plot.

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