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MatroZZZ [7]
2 years ago
8

Mary and Jane both took PSY230 last semester but they were in two different sections taught by different professors. Mary's clas

s had a mean grade of 85 points with a standard deviation of 5 points while Jane's class had a mean grade of 80 points with a standard deviation of 10 points. Both Mary and Jane earned 88 points. Who did better in class
Mathematics
1 answer:
Greeley [361]2 years ago
4 0

Answer:

Due to the higher z-score, Jane did better in class.

Step-by-step explanation:

Z-score:

In a set with mean \mu and standard deviation \sigma, the zscore of a measure X is given by:

Z = \frac{X - \mu}{\sigma}

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

In this question:

Whoever's grade had the better z-score did better in class.

Mary's:

Mean of 85, standard deviation of 5, grade of 88. This means that \mu = 85, \sigma = 5, X = 88. So

Z = \frac{X - \mu}{\sigma}

Z = \frac{88 - 85}{5}

Z = 0.6

Jane's

Mean of 80, standard deviation of 10, grade of 88. This means that \mu = 80, \sigma = 10, X = 88. So

Z = \frac{X - \mu}{\sigma}

Z = \frac{88 - 80}{10}

Z = 0.8

Due to the higher z-score, Jane did better in class.

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x1=c(16.7,17.4,18.4,16.8,18.9,17.1,17.3,18.2,21.3,21.2,20.7,18.5)

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lm(formula = y ~ x1 + x2)

Residuals:  

   Min      1Q Median      3Q     Max

-41.730 -12.174   0.791 12.374 40.093

Coefficients:

        Estimate Std. Error t value Pr(>|t|)    

(Intercept) 415.113     82.517   5.031 0.000709 ***  

x1            -6.593      4.859 -1.357 0.207913    

x2            -4.504      1.071 -4.204 0.002292 **  

---  

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

Residual standard error: 24.45 on 9 degrees of freedom  

Multiple R-squared: 0.768,     Adjusted R-squared: 0.7164  

F-statistic: 14.9 on 2 and 9 DF, p-value: 0.001395

a).  y=415.113 +(-6.593)x1 +(-4.504)x2

b). s=24.45

c).  y =415.113 +(-6.593)*21.3 +(-4.504)*43 =81.0101

residual =68-81.0101 = -13.0101

d). F=14.9

P=0.0014

There is convincing evidence at least one of the explanatory variables is significant predictor of the response.

e).  newdata=data.frame(x1=21.3, x2=43)

# confidence interval

predict(mod, newdata, interval="confidence")

#prediction interval

predict(mod, newdata, interval="predict")

confidence interval

> predict(mod, newdata, interval="confidence",level=.95)

      fit      lwr      upr

1 81.03364 43.52379 118.5435

95% CI = (43.52, 118.54)

f).  #prediction interval

> predict(mod, newdata, interval="predict",level=.95)

      fit      lwr      upr

1 81.03364 14.19586 147.8714

95% PI=(14.20, 147.87)

g).  No, there is not evidence this factor is significant. It should be dropped from the model.

4 0
2 years ago
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