SALES REPRESENTATIVE| NUMBER OF UNITS SOLD| NUMBER OF SALES CALLS| A| 28| 14|
B| 66| 35|
C| 38| 22|
D| 70| 29|
E| 22| 6|
F| 27| 15|
G| 28| 17|
H| 47| 20|
I| 14| 12|
J| 68| 29|
| | |
| | |

a) draw a scatter diagram of number of sales calls and number of units sold

b) Estimate a simple linear regression model to explain the relationship between number of sales calls and number of units sold y=2.139x-1.760
Number of units sold=2.139Number of units sold-1.760
c) Calculate and interpret the coefficient of correlation r=0.853=0.9236 (There is strong correlation between two variables as its near 1) d) the coefficient of determination
r2=0.853(The magnitude of the coefficient of determination indicates the proportion of variance in one variable, explained from knowledge of the second variable) e) the standard error of estimate

S.E=0.3133(The standard error is the estimated standard deviation of a statistic) f) Conduct a test of hypothesis to determine whether the coefficient of correlation in the population is zero H0:β1=0

Ha:β1≠0

t=β1SE =6.826
p-value for df=9 and t=6.826:0.001

0.0001<0.05
Therefore null hypothesis is rejected
Hence coefficient of correlation is zero is rejected
Therefore there is significant relationship between number of sales calls and number of units sold. g) Construct and interpret confidence intervals and prediction intervals for the dependent variable, number of units sold. Confidence interval:

(x-tsn,x+tsn)
Confidence interval for number of sales calls:

(x-tsn,x+tsn)
(0.924, 2.8612)

CALCULATIONS ON EXCEL

Regression Analysis| | | | | | |
| | | | | | | |
| r² | 0.853 | n | 10 | | | |
| r | 0.924 | k | 1 | | | |
| Std. Error | 8.412 | Dep. Var. | NUMBER OF UNITS SOLD| | | | | | | | | |
ANOVA table| | | | | | | |
Source| SS | df | MS| F| p-value...

...Chapter 9
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b. Functional. For a given value of X there is one unique value of Y.
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...l
RegressionAnalysis
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From:
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Various hypothesistests were compared as well as several multiple regressions in order to identify the factors that would manipulate the selling price of Ford Mustangs. The data being used contains...

...CHAPTER 16
SIMPLE LINEAR REGRESSION
AND CORRELATION
SECTIONS 1 - 2
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In the following multiple-choice questions, please circle the correct answer.
1. The regression line [pic] = 3 + 2x has been fitted to the data points (4, 8), (2, 5), and (1, 2). The sum of the squared residuals will be:
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Correlation only indicates the degree and direction of relationship between two variables. It does not, necessarily connote a cause-effect relationship. Even when there are grounds to believe the causal relationship exits, correlation does not tell us which variable is the cause and which, the effect. For example, the demand for a commodity and its price will generally be found to be correlated, but the question whether demand depends on...

...significant influences on the business cycle. This paper tries to figure out the determinants of the selling price of houses in Oregon. The data set used in this paper has been retrieved from the case study titled “Housing Price” (Case #27 - Practical Data Analysis: Case Studies in Business Statistics- Marlene A. Smith & Peter G. Bryant)
The most important factor in determining the selling prices ofhouses is to know the features that drive the selling prices of the house....

...5645 | 3.17 | 32.11 |
2010 | 4284 | 3.28 | 31.23 |
2011 | 3674 | 2.65 | 24.16 |
Using regressionanalysis we want to determine the relationship between ROA, ROE and stock price of PT BCA Tbk. In this case, ROA and ROE are the independent or explanatory variable (X), while stock price is the dependent variable that we want to explain (Y).
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SUMMARY OUTPUT |
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