United States University Selecting a Point and Shoot Digital Camera Case Study

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natry27

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United States University

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Read the Case Problem 3 (Selecting a Point and Shoot Digital Camera) presented at the end of Chapter 14 in the textbook. Answer the following questions:

  1. Develop numerical summaries of the data.
  2. Using overall score as the dependent variable, develop three scatter diagrams,

one using price as the independent variable, one using the number of megapixels as the independent variable, and one using weight as the independent variable. which of the three independent variables appears to be the best predictor of overall score?

  1. Using simple linear regression, develop an estimated regression equation that could be used to predict the overall score given the price of the camera. For this estimated regression equation, perform an analysis of the residuals and discuss your findings and conclusions.
  2. Analyze the data using only the observations for the Canon cameras. Discuss the appropriateness of using simple linear regression and make any recommendations regarding the prediction of overall score using just the price of the camera.

Provide your work in detail and include in-text citations. Include a graph of the regression line in the scatterplot.

Please be sure to include in-text citations and peer reviewed references in APA format in your discussion post.

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Explanation & Answer

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CASE 3-Selecting a Point and Shoot Digital Camera
Abstract
The paper studied two camera brands and rated them based on their weight, megapixel
among other factors. The camera scores were provided on a scale of 1-1oo where 100 was the
best. The study ventured to define if the price of a camera was enhanced by how good the gadget
was.
Introduction
The problem studied in the paper was if or not the quality of the camera influences on
price. Another issue that is studied is how the score is formed. It is essential to define if a
particular variable enhances the camera’s score. Also, it is significant to define if a score is
provoked solely by weight, megapixels, or a mixture of the variables. The report tested 166
point-and-shoot digital cameras with much emphasis laid on weight, megapixel among other
factors. The cameras were scored on a scale of 1-100 with the higher number being the better.
The question that was being studied is whether consuming more cash on a camera will result in
having a better camera. On the other hand, the question that is being examined is if cameras
which have more megapixels are expensive. In this paper, 15 Nikon and 13 Canon cameras were
examined.
Analysis
Data was passed into Minitab where many graphs and statistics were created to examine
the data. Price and score were selected as the responses or rather independent variables. Weight
and megapixels were characterized as predictors or rather independent variables. Graphs which
were created included normal probability of residuals, scatterplots as well as residual versus fits

graphs. All the three scatterplots compare score to another variable. The variables which were
compare to score include price, megapixel, and weight. The graph which is versus fits help in
showing the outliers. Points which are below and above the horizontal line are all outliers. All
versus fits graphs posses a non-constant variance. The plot named normal probability plot
expresses if or not a data set is normally distributed. Any deviation from the line implies a
departure from ordinariness. Regression are;
Price = 42.2 + .186 Megapixels + 2.01 weight (Oz.)
As well as Score = -142 + 9.76 megapixels + 33 weight (Oz.).
However, residuals have a random pattern, and based on this, it implies a good fit for a linear
model.
Regression Analysis: Price ($) versus Megapixels, Weight (oz.)
Analysis of Variance

Source
Regression
Megapixels
weight (oz)
Error
Lack-of-fit
Pure error
Total

DF
2
1
1
25
10
15
27

Adj SS
30880
8359
27308
154217
19455
134762
185096

Adj MS F-Value
15440
2.5
8359
1.36
27308
4.43
6169
1946
0.22
8984

P-value
0.102
0.255
0.046
0.99

Model Summary
S
R-sq R-sq (adj) R-sq (pred)
78.5409
16.68% 10.02%
0.00%
Coefficients

Term
Coef SE
Constant
-1.42
Megapixels
9.76
Weight (oz)
33

Coef
155
8.38
15.7

T-Value
-0.92
1.16
2.1

P-Value
0.368
0.255
0.046

VIF
1.04
1.04

Regression Equation
Price ($) = -142 + 9.76 + 33.0

Fits and Diagnostics for Unusual Observations
Std
Obs Price ($) Fit Resid Resid
17 400.0 225.4 174.6 2.37 R
R Large residual

Regression Analysis: Score versus Megapixels, Weight (oz.)
Analysis of Variance
Source
Regression
Megapixels
Weight (oz.)
Error
Lack-of-Fit
Pure Error
Total

DF
2
1
1
25
10
15
27

Adj SS
101.83
3.04
101.76
1108.60
413.73
694.87
1210.43

Adj MS
50.914
3.035
101.755
44.344
41.373
46.324

F-Value
1.15
0.07
2.29

P-Value
0.333
0.796
0.142

0.89

0.560

Model Summary
S
6.65913

R-sq
8.41%

R-sq(adj)
1.09%

R-sq(pred)
0.00%

Coefficients
Term
Constant
Megapixels
Weight (oz.)

Coef
...


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