A university wants to compare out-of-state applicants' mean SAT math scores (μ1) to in-state applicants' mean SAT math scores (μ2). The university looks at 35 in-state applicants and 35 out-of-state applicants. The mean SAT math score for in-state applicants was 540, with a standard deviation of 20. The mean SAT math score for out-of-state applicants was 555, with a standard deviation of 25. It is reasonable to assume the corresponding population standard deviations are equal. To calculate the confidence interval for the difference μ1 − μ2, what is the number of degrees of freedom of the appropriate probability distribution?

64

64.87

68

69

Answer :

Answer:

[tex]df=n_1 +n_2 -1=35+35-2=68[/tex]  

Step-by-step explanation:

Previous concepts  

A confidence interval is "a range of values that’s likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval".  

The margin of error is the range of values below and above the sample statistic in a confidence interval.  

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".  

[tex]\bar X_1 =540[/tex] represent the sample mean 1  (in-state applicants)

[tex]\bar X_2 =555[/tex] represent the sample mean 2  (out-of state applicants)

n1=35 represent the sample 1 size  (in-state applicants)

n2=35 represent the sample 2 size  (out-of state applicants)

[tex]s_1 =20[/tex] sample standard deviation for sample 1  (in-state applicants)

[tex]s_2 =25[/tex] sample standard deviation for sample 2  (out-of state applicants)

[tex]\mu_1 -\mu_2[/tex] parameter of interest.

We are assuming that the population standard deviations are equal.

The confidence interval for the difference of means is given by the following formula:  

[tex](\bar X_1 -\bar X_2) \pm t_{\alpha/2}s_p \sqrt{\frac{1}{n_1}+\frac{1}{n_2}}[/tex] (1)

Where

[tex]s^2=\frac{(n_1 -1)s_1^2 +(n_2-1)s_2^2}{n_1 +n_2 -2}[/tex]  

[tex]s^2=\frac{(35 -1)20^2 +(35-1)25^2}{35 +35 -2}=512.5[/tex]

[tex]s=\sqrt{512.5}=22.638[/tex]

The point of estimate for [tex]\mu_1 -\mu_2[/tex] is just given by:

[tex]\bar X_1 -\bar X_2 =540-555=-15[/tex]

In order to calculate the critical value [tex]t_{\alpha/2}[/tex] we need to find first the degrees of freedom, given by:  

[tex]df=n_1 +n_2 -1=35+35-2=68[/tex]  

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