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Statistics Quiz: Correlation and Regression, Assignments of Mathematics

DeVry Institute of Technology & Keller Graduate School of Management - New YorkMathematics

This quiz focuses on key concepts in statistics, specifically correlation and regression analysis. It covers topics like the coefficient of correlation, its interpretation, and the relationship between variables. The quiz also explores regression models, including the least squares regression line, prediction of dependent variables, and confidence intervals. It provides a comprehensive assessment of understanding in these areas.

Typology: Assignments

2023/2024

Available from 12/18/2024

Milestonee
Milestonee 🇺🇸

4.4

(22)

3.5K documents

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WEEK 6 QUIZ
1.
A value of -1 for the coefficient of correlation between two
variables means that the two variables are .
not related at
all
perfectly
related
weakly
related
very weakly related
somewhat strongly related
2.
If there is positive correlation between two sets of numbers,
then
.
SSE = 1
r
=
0
MSE = 1
r
>
0
r
<
0
3.
If the correlation coefficient between variables X and Y is
roughly zero, then .
Y is independent of
X
Y is dependent
on X
there is a linear correlation between Y
and X
Y is not necessarily independent of
X
Y is caused by X.
4.
Determine the Pearson product-moment correlation
pf3
pf4
pf5
pf8

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Download Statistics Quiz: Correlation and Regression and more Assignments Mathematics in PDF only on Docsity!

WEEK 6 QUIZ

  1. A value of -1 for the coefficient of correlation between two variables means that the two variables are. not related at all perfectly related weakly related very weakly related somewhat strongly related
  2. If there is positive correlation between two sets of numbers, then . SSE = 1 r = 0 MSE = 1 r > 0 r < 0
  3. If the correlation coefficient between variables X and Y is roughly zero, then. Y is independent of X Y is dependent on X there is a linear correlation between Y and X Y is not necessarily independent of X Y is caused by X.
  4. Determine the Pearson product-moment correlation

coefficient for the following data. x 1 12 9 6 5 3 2 y 9 4 4 5 7 7 9

results in the smallest sum of errors squared

results in zero sum of errors squared maximizes the sum of errors squared passes through 95% the data points

  1. The following regression model was fitted to sample data with 12 observations: y = 30 + 4.50 x. What is the predicted value of y for a given value of x = 6?
    30
    36
  2. For a least squares regression line, the sum of the residuals is . always negative always positive sometimes positive and sometimes negative always zero
  3. If the standard error of the estimate for a regression model fitted to a large number of paired observations is 1.75, approximately 95% of the residuals would lie within. −1.75 and +1. −3.50 and +3. −0.68 and +0. −0.97 and +0. −0.95 and +0.
  4. If x and y in a regression model are totally unrelated,. the coefficient of determination would be 0 the coefficient of determination would be 1

following model was developed: y = 2,000 + 0.42 x. If a car is driven 30,000 miles, the predicted cost is. 32,

  1. A manager wants to predict the cost ( y ) of travel for salespeople based on the number of days ( x ) spent on each sales trip. The following model has been developed: y = $400 + 120x. If a trip took 4 days, the predicted cost of the trip is. 480 1080 880 2080 524