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...

...REGRESSIONANALYSIS
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...

...Quantitative Methods Project
RegressionAnalysis for the pricing of players in the
Indian Premier League
Executive Summary
The selling price of players at IPL auction is affected by more than one factor. Most of these factors affect each other and still others impact the selling price only indirectly. The challenge of performing a multiple...

...RegressionAnalysis (Tom’s Used Mustangs)
Irving Campus
GM 533: Applied Managerial Statistics
04/19/2012
Memo
To:
From:
Date: April 19st, 2012
Re: Statistic Analysis on price settings
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...

...you
cannot consult the regression R2 because
(a) ln(Y) may be negative for 0 < Y < 1.
(b) the TSS are not measured in the same units between the two models.
(c) the slope no longer indicates the effect of a unit change of X on Y in the log-linear
model.
(d) the regression R2 can be greater than one in the second model.
1
(v) The exponential function
(a) is the inverse of the natural logarithm function.
(b) does not play an important role in modeling nonlinear...

...l
RegressionAnalysis
Basic Concepts & Methodology
1. Introduction
Regressionanalysis is by far the most popular technique in business and economics for
seeking to explain variations in some quantity in terms of variations in other quantities, or to
develop forecasts of the future based on data from the past. For example, suppose we are
interested in the monthly sales of retail outlets across the UK. An initial data...

...
Mortality Rates
RegressionAnalysis of Multiple Variables
Neil Bhatt
993569302
Sta 108 P. Burman
11 total pages
The question being posed in this experiment is to understand whether or not pollution has an impact on the mortality rate. Taking data from 60 cities (n=60) where the responsive variable Y = mortality rate per population of 100,000, whose variables include Education, Percent of the population that is...

...Chapter 9
RegressionAnalysis
1. a. Y = 250 + 3 X
b. Functional. For a given value of X there is one unique value of Y.
2. The model with the highest R2 might actually "overfit" the data and not provide accurate predictions. The R2 statistic can be inflated (or made arbitrarily large) by including superfluous independent variables in the model. If this happens the predictive ability of the model will actually be degraded since the model is biased...

...
Unit 5 – RegressionAnalysis
Mikeja R. Cherry
American InterContinental University
Abstract
In this brief, I will demonstrate selected perceptions of the company Nordstrom, Inc., a retailer that specializes in fashion apparel with over 12 million dollars in sales last year. I will research, review, and analyze perceptions of the company, create graphs to show qualitative and quantitative analysis, and provide a summary of my findings....

2108 Words |
11 Pages

Share this Document

Let your classmates know about this document and more at StudyMode.com

## Share this Document

Let your classmates know about this document and more at StudyMode.com