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Give a two-way table and specify a particular cell for that table. In each case find the expected count for that cell and the contribution to the chi- square statistic for that cell. \((\mathrm{B}, \mathrm{E})\) cell $$ \begin{array}{l|rrrr|r} \hline & \text { D } & \text { E } & \text { F } & \text { G } & \text { Total } \\ \hline \text { A } & 39 & 34 & 43 & 34 & 150 \\ \text { B } & 78 & 89 & 70 & 93 & 330 \\ \text { C } & 23 & 37 & 27 & 33 & 120 \\ \hline \text { Total } & 140 & 160 & 140 & 160 & 600 \\ \hline \end{array} $$

Short Answer

Expert verified
The expected count for cell (B, E) is 88 and its contribution to the chi-square statistic is 0.0114.

Step by step solution

01

Calculate the Expected Count

To calculate the expected count for cell (B, E), multiply the total for row B (330) by the total for column E (160), then divide by the overall total (600). So, the expected count \(E(B,E) = \frac{{330 \times 160}}{{600}} = 88\).
02

Calculate the Contribution to the Chi-Square Statistic

To calculate the contribution to the chi-square statistic for cell (B, E), first find the difference between the observed count (O = 89) and the expected count (E = 88). Then square this difference and divide by the expected count. So, the Chi-square contribution \(\chi^2(B,E) = \frac{{(O - E)^2}}{{E}} = \frac{{(89 - 88)^2}}{{88}} = 0.0114\).

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Most popular questions from this chapter

Binge Drinking The American College Health Association - National College Health Assessment survey \(,{ }^{17}\) introduced on page 60 , was administered at 44 colleges and universities in Fall 2011 with more than 27,000 students participating in the survey. Students in the ACHA-NCHA survey were asked "Within the last two weeks, how many times have you had five or more drinks of alcohol at a sitting?" The results are given in Table 7.31 . Is there a significant difference in drinking habits depending on gender? Show all details of the test. If there is an association, use the observed and expected counts to give an informative conclusion in context. $$ \begin{array}{c|rr|r} \hline & \text { Male } & \text { Female } & \text { Total } \\ \hline 0 & 5,402 & 13,310 & 18,712 \\ 1-2 & 2,147 & 3,678 & 5,825 \\ 3-4 & 912 & 966 & 1,878 \\ 5+ & 495 & 358 & 853 \\ \hline \text { Total } & 8,956 & 18,312 & 27,268 \\ \hline \end{array} $$

In Exercises 7.5 to 7.8 , the categories of a categorical variable are given along with the observed counts from a sample. The expected counts from a null hypothesis are given in parentheses. Compute the \(\chi^{2}\) -test statistic, and use the \(\chi^{2}\) -distribution to find the p-value of the test. $$ \begin{array}{lccc} \hline \text { Category } & \text { A } & \text { B } & \text { C } \\ \text { Observed } & 35(40) & 32(40) & 53(40) \\ \text { (Expected) } & & & \\ \hline \end{array} $$

7.53 Testing Genetic Alleles for Fast-Twitch Muscles The study on genetics and fast-twitch muscles includes a sample of elite sprinters, a sample of elite endurance athletes, and a control group of nonathletes. Is there an association between genetic allele classification \((R\) or \(X)\) and group (sprinter, endurance, control)? Computer output is shown for this chi-square test. In each cell, the top number is the observed count, the middle number is the expected count, and the bottom number is the contribution to the chi- square statistic. \(\begin{array}{lrrr} & \text { R } & \text { X } & \text { Total } \\ \text { Control } & 244 & 192 & 436 \\ & 251.42 & 184.58 & \\ & 0.219 & 0.299 & \\\ \text { Sprint } & 77 & 30 & 107 \\ & 61.70 & 45.30 & \\ & 3.792 & 5.166 & \\\ \text { Endurance } & 104 & 90 & 194 \\ & 111.87 & 82.13 & \\ & 0.554 & 0.755 & \\ \text { Total } & 425 & 312 & 737\end{array}\) \(\mathrm{Ch} \mathrm{i}-\mathrm{Sq}=10.785, \mathrm{DF}=2, \mathrm{P}\) -Value \(=0.005\) (a) How many endurance athletes were included in the study? (b) What is the expected count for sprinters with the \(R\) allele? For this cell, what is the contribution to the chi-square statistic? Verify both values by computing them yourself. (c) What are the degrees of freedom for the test? Verify this value by computing it yourself. (d) What is the chi-square test statistic? What is the p-value? What is the conclusion of the test? (e) Which cell contributes the most to the chisquare statistic? For this cell, is the observed count greater than or less than the expected count? (f) Which allele is most over-represented in sprinters? Which allele is most over-represented in endurance athletes?

Favorite Skittles Flavor? Exercise 7.13 on page 518 discusses a sample of people choosing their favorite Skittles flavor by color (green, orange, purple, red, or yellow). A separate poll sampled 91 people, again asking them their favorite Skittles flavor, but rather than by color they asked by the actual flavor (lime, orange, grape, strawberry, and lemon, respectively). \(^{19}\) Table 7.32 shows the results from both polls. Does the way people choose their favorite Skittles type, by color or flavor, appear to be related to which type is chosen? (a) State the null and alternative hypotheses. (b) Give a table with the expected counts for each of the 10 cells. (c) Are the expected counts large enough for a chisquare test? (d) How many degrees of freedom do we have for this test? (e) Calculate the chi-square test statistic. (f) Determine the p-value. Do we find evidence that method of choice affects which is chosen? $$ \begin{array}{lcrccc} \hline & \begin{array}{l} \text { Green } \\ \text { (Lime) } \end{array} & \begin{array}{c} \text { Purple } \\ \text { Orange } \end{array} & \begin{array}{c} \text { Red } \\ \text { (Grape) } \end{array} & \begin{array}{c} \text { Yellow } \\ \text { (Strawberry) } \end{array} & \text { (Lemon) } \\ \hline \text { Color } & 18 & 9 & 15 & 13 & 11 \\ \text { Flavor } & 13 & 16 & 19 & 34 & 9 \end{array} $$

Gender and ACTN3 Genotype We see in the previous two exercises that sprinters are more likely to have allele \(R\) and genotype \(R R\) versions of the ACTN3 gene, which makes these versions associated with fast-twitch muscles. Is there an association between genotype and gender? Computer output is shown for this chi-square test, using the control group in the study. In each cell, the top number is the observed count, the middle number is the expected count, and the bottom number is the contribution to the chi-square statistic. What is the p-value? What is the conclusion of the test? Is gender associated with the likelihood of having a "sprinting gene"? \(\begin{array}{lrrrr} & \text { RR } & \text { RX } & \text { XX } & \text { Total } \\ \text { Male } & 40 & 73 & 21 & 134 \\ & 40.26 & 69.20 & 24.54 & \\\ & 0.002 & 0.208 & 0.509 & \\ \text { Female } & 88 & 147 & 57 & 292 \\ & 87.74 & 150.80 & 53.46 & \\ & 0.001 & 0.096 & 0.234 & \\ \text { Total } & 128 & 220 & 78 & 426\end{array}\) \(\mathrm{Chi}-\mathrm{Sq}=1.050, \mathrm{DF}=2, \mathrm{P}\) -Value \(=0.592\)

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