A sample of 50 offered a sample expect of 19.4. The population standard deviation in >2. the hypothesis is a reduced tail test: HO: M >= 20 HA: M What is the test statistic?

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A sample of 50 offered a sample suppose of 19.4. The populace typical deviation in >2. the hypothesis is a reduced tail test: HO: M >= 20 HA: M If the alpha worth is .05, what is the table value?

A sample of 50 offered a sample suppose of 19.4. The populace typical deviation in >2. the hypothesis is a reduced tail test: HO: M >= 20 HA: M What is your conclusion?

Assume a top tailed test for a population value of 12. A sample of 25 provided a sample expect of 14 and also a traditional deviation of 4.32. What is the test statistic?

Assume a top tailed test for a population worth of 12. A sample of 25 gave a sample mean of 14 and a standard deviation of 4.32. What are the degrees of freedom?

Assume a upper tailed test for a populace value of 12. A sample of 25 provided a sample intend of 14 and a conventional deviation of 4.32. If the table value is 1.711, what is the conclusion?

Assume: HO: M=18 HA: M(doesnt =) 18 N=48 alpha=0.05 Xbar= 17 SD=4.5 t-value=2.012 What are the degrees of freedom?

Assume: HO: M=18 HA: M(doesnt =) 18 N=48 alpha=0.05 Xbar= 17 SD=4.5 t-value=2.012 What alpha value would be supplied to look up the t-value?

Assume: HO: M=18 HA: M(doesnt =) 18 N=48 alpha=0.05 Xbar= 17 SD=4.5 t-value=2.012 What is the test statistic?

Assume: HO: M=18 HA: M(doesnt =) 18 N=48 alpha=0.05 Xbar= 17 SD=4.5 t-value=2.012 Which of the complying with is correct?

Assume: HO: M=18 HA: M(doesnt =) 18 N=48 alpha=0.05 Xbar= 17 SD=4.5 t-value=2.012 If the test statistic is -1.6, what is your conclusion?

Assume: HO: M>= 10 HA: M Population SD=5 alpha=0.05 Standard error of Xbar=0.46HO is rejected id XbarIf the populace mean is 9, what is the probcapability that the sample intend leads to the conclusion "execute not refuse HO"?

Assume: HO: M>= 10 HA: M Population SD=5 alpha=0.05 Standard error of Xbar=0.46HO is rejected id XbarWhat form of error would certainly be made if the actual population mean is 9 and we conclude that HO: M>=10 is true?

Assume: HO: M>= 10 HA: M Population SD=5 alpha=0.05 Standard error of Xbar=0.46HO is rejected id XbarWhat is the probcapability of making a Type II Error if the actual populace expect is 8?

SAMPLE 1 SAMPLE 2 HO: M1-M2N1=40 N2=50 HA: M1-M2>0Xbar1=25.2 Xbar2=22.8 alpha=0.05Sigma1=5.2 Sigma2=6.0What is the test statistic?

SAMPLE 1 SAMPLE 2 HO: M1-M2N1=40 N2=50 HA: M1-M2>0Xbar1=25.2 Xbar2=22.8 alpha=0.05Sigma1=5.2 Sigma2=6.0What is the table z value?

SAMPLE 1 SAMPLE 2 HO: M1-M2N1=40 N2=50 HA: M1-M2>0Xbar1=25.2 Xbar2=22.8 alpha=0.05Sigma1=5.2 Sigma2=6.0What is your conclusion?

A sample of 16 items provides a sample SD of 9.5. alpha=0.05. The hypotheses are HO: populace variance(sigma squared)H1: population variance (sigma squared)>50What is the test statistic?

A sample of 16 items gives a sample SD of 9.5. alpha=0.05. The hypotheses are HO: population variance(sigma squared)H1: populace variance (sigma squared)>50If the table Chi Square worth is 24.996, what is your conclusion?

Given the complying with information:Xi 3 12 6 20 14Yi 55 40 50 10 15Develop the approximated regression equation by completing the variables of b0 and also b1.

The regression equation is Time=0.930 + 0.388 Months + 1.26 Type (a bunch of random numbers)S=0.459048 R-sq=85.9% R-sq(adj)=81.9%alpha=0.05 the Ftable worth for alpha=0.05 is F,2,7, is 19.35The table worth for t (alpha/2), 7 is 2.365Which of the complying with are true?(1)

The regression equation is Time=0.930 + 0.388 Months + 1.26 Type (a bunch of random numbers)S=0.459048 R-sq=85.9% R-sq(adj)=81.9%alpha=0.05 the Ftable worth for alpha=0.05 is F,2,7, is 19.35The table worth for t (alpha/2), 7 is 2.365Which of the adhering to is true?(2)

The regression equation is Time=0.930 + 0.388 Months + 1.26 Type (a bunch of random numbers)S=0.459048 R-sq=85.9% R-sq(adj)=81.9%alpha=0.05 the Ftable worth for alpha=0.05 is F,2,7, is 19.35The table value for t (alpha/2), 7 is 2.365How much variability in the dependent variable was explained?

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