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by: Nikita Hendricks

QBA 2 2305

Nikita Hendricks

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8/30/16 Chapter 1 Notes Scatter plot and Correlation
Prof. Turner
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This 3 page Class Notes was uploaded by Nikita Hendricks on Tuesday August 30, 2016. The Class Notes belongs to 2305 at Baylor University taught by Prof. Turner in Fall 2016. Since its upload, it has received 37 views. For similar materials see QBA in Business at Baylor University.

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Date Created: 08/30/16
QUANTATIVE BUSINESS ANALYSIS 11 2305 PROF. TURNER CLASS NOTES Chapter 1 Scatterplots & Correlation A. Two rules for hypothesis testing 1. the equal sign ALWAYS goes in the null hypothesis 2. If the p-value is low, reject Ho B. purpose of p-value in hypothesis testing the p-value is the actual chance that i reject the null hypothesis when in fact it is true. if thep-value is lower than .05 reject the Ho. 1. What is a scatterplot Plots one quantitative variable against another. It is usually the first step leading to regression analysis. The direction of the patterns indicated whether there is a positive, negative, or non-relationship between the variables. Coefficient of correlation: (r) is a measure of the strength of the relationship between two variables. Shows direction and strength of the linear relationship between two interval variables. -1 to 1. A correlation of indicates no relationship between X and Y. A value of -1 or +1 indicates a perfect linear relationship between X and Y The sign simply implies which direction the slope is taking. Simple Linear Regression: Only using one x variable/ predictor Coefficient of determination (r^2) is the proportion of the total variation in the dependent variable (Y) that is explained or accounted for by the variation in the independent variable (X). It is the square of the coefficient of correlation. Ranges from 0 to 1 doesn't show direction Suspicious outlier Between I2I and I3I Severe outlier I3I and should be removed from the data Extrapolation Using values beyond the range of the given X to predict Y. NEVER say X Causes Y unless you’ve done a controlled experiment. 2. Variables X is the independent variable. Using descriptive statistics to predict y Y is the Dependent variable; the value we wish to predict 3. Regression Line Also known as Least Squares Equation, Line of Best Fit A. Regression Analysis: The study of the equational relationship between X and Y. B. Correlation Analysis: The study of the nature and degree of the relationship between variables. C. Confidence Interval on slope: If Zero falls in the interval on the slope, then that’s a possible value for the slope and x is not a good predictor of y. 4. Standard Error: The smaller the standard error the closer the points will be to the regression line, the better the predictive power of line. Interpret the standard error by using the Empirical Rule: Approximately 95% of the predicted values will be within +/- 2 standard error. 5. Residuals: These are the differences between the observed values of Y at X and the Predicted values of Y at that same X. Residuals should have a mean of Zero and a random pattern in their plot.


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