Assignment:
Q1-Testing for categories with different proportions. Here are the observed frequencies from four categories; 5,10,10,20. Assume that we want to use a 0.05 significance level to test the claim that the four categories have proportions of 0.20, 0.25, 0.25and 0.30, repectively
What is the null hypothesis?
What are the expected frequencies for the four categories?
What is the value of the test statistic?
What is the critical value?
What do you conclude about the given claim?
Q2-No smoking
The accompanying table summarizes successes and failure when subjects used different methods in trying to stop smoking. The determination of smoking or not smoking was made five month after the treatment was begun, and the data are based on results from the center of disease control and prevention. Use the TI-83/84Plus result {see below} with a 0.05significance level to test the claim that success is independent of the method used. If someone wants to stop smoking, does the choice of the method make a difference?
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nicotine gum
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Nicotine patch
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smoking
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191
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263
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non smoking
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59
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57
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TI-83/84 Plus
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X-Test
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X(X)=2.900233793
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p=.0885667054
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df=1
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Q3-Solar energy in different weather.
A student of the author lives in home with solar electric system. At the same time each day ,she collected voltage readings from a meter connected to the system and analysis of variance was used with readings obtained on three different types of day: sunny, cloudy, and rainy. The TI-83/84 plus the calculator results are in the margin. Use a 0.05 significance level to test the claim that the mean voltage reading is the same for the three different of day. Is there sufficient evidence to support a claim of different population means? we might expect that a solar system would provide more electric energy on sunny days than on cloudy or rainy days. Can we conclude that sunny days result in greater amounts of electric energy?
TI-83/84
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One -way ANOVA
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F=38. 03789731
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P=1. 3340195e-6
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Factor
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df=15
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SS=1.36333333
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↓ MS=3.45722222
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One -way ANOVA
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Ms=3.4572222
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Error
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df=2
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SS=6. 91444444
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Sxp=.301477841
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4-use the excel display which results from the scores listed in table below the sample data are SAT scores on the verbal and math portions of SAT-I and are based on reported statistics from the College Board
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verbal
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female
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646
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539
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348
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623
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478
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429
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298
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782
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626
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533
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male
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562
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525
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512
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576
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570
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480
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571
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555
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519
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596
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math
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female
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484
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489
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436
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396
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545
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504
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574
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352
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365
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350
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male
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547
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678
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464
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651
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645
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673
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624
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624
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328
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548
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ANOVA
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source of variation
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SS
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df
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MS
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F
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P-Value
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F crit
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Sample
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52635.03
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1
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52635.03
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5.029517
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0.031169
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4.113161
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Columns
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6027.025
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1
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6027.025
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0.57591
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0.45286
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4.113161
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Interaction
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31528.22
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1
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31528.22
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3.012666
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0.09117
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4.113161
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Within
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376748.1
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36
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10465.23
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Total
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466938.4
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39
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1: Interaction effect - test the null hypothesis that SAT scores are not affected by an interaction between gender and test (verbal/math). What do you conclude?
2: Effect of Type of SAT Test- Assume that SAT scores are not affected by an interaction between gender and the type of test (verbal/Math). Is there sufficient evidence to support the claim that gender has an effect on SAT scores?