This is the step1, so step 2 and step 3 and Step 4 must be answered. (Step 2: Test Statistic -Pearson's r correlation coefficient AND Step 3: Critical Values, and Step 4: Solve For the test stat) so based it from the photo. The table shows the number of hours spend by 30 students in studing and their test scores. Solve for the Pearson’s r and interpret the results. Use 5% significance level. Step 1: Null and Alternative Hypothesis Ho: r = 0; There is no significant relationship between the number of hours spent studying and the students test scores. Ha: r ≠ 0; There is a significant relationship between the number of hours spent studying and the students test scores. Step 2: Test Statistic -P

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Assume that you have paired values consisting of heights (in inches) and weights (in lb) from 40 randomly selected men. The linear correlation coefficient r is 0.594. Find the value of the coefficient of determination. What practical information does the coefficient of determination provide? Choose the correct answer below. O A. The coefficient of determination is 0.353. 64.7% of the variation is explained by the linear correlation, and 35.3% is explained by other factors. O B. The coefficient of determination is 0.647. 35.3% of the variation is explained by the linear correlation, and 64.7% is explained by other factors. O C. The coefficient of determination is 0.647. 64.7% of the variation is explained by the linear correlation, and 35.3% is explained by other factors. O D. The coefficient of determination is 0.353. 35.3% of the variation is explained by the linear correlation, and 64.7% is explained by other factors.

topic is Hypothesis test for proportions. I already answered most of the question. Please help me fill the rest. Thanks

Justin wants to know whether a commonly prescribed drug does improve the attention span of students with attention deficit disorder (ADD). He knows that the mean attention span for students with ADD who are not taking the drug is 2.3 minutes long. His sample of 12 students taking the drug yielded a mean of 4.6 minutes. Justin can find no information regarding σx , so he calculated s2x =1.96. a. Identify the independent and dependent variables.  b. In a sentence, state the null hypothesis being tested.  c. Using symbols, state the null and alternative hypotheses.  d. Determine the critical region using a one-tailed test with alpha = .05. e .Conduct the hypothesis test (Do the math and compare the t-critical and t-obtained values). f. State your conclusions in terms of H0 (Should you reject the H0 or fail to reject/accept the H0). g. Based on your analysis, is there a relationship between the drug and attention span?

You read a newspaper article that says that 74% of households in your town participate in a recycling program.  To test that statement at the 10% significance level, you randomly select 54 households in town and you find that 35 of them participate in the recycling program. Ho: P=0.74                  Ha:P=/ 0.74 Find the P-value.  Round your answer to 3 decimal places.

You wish to determine if there is a linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.01 and round all values to 4 decimal places. Driver Age 65 69 48 37 49 27 62 64 Ho: p = 0 Ha: p = 0 r= Find the p-value p-value= Number of Driver Deaths per 100,000 22 22 34 Find the Linear Correlation Coefficient The p-value is Greater than a Less than (or equal to) a 23 28 20 The p-value leads to a decision to O Do Not Reject Ho O Accept Ho O Reject Ho 33 33 The conclusion is O There is a significant negative linear correlation between driver age and number of driver deaths. There is a significant linear correlation between driver age and number of driver deaths. O There is insufficient evidence to make a conclusion about the linear correlation between driver age and number of driver deaths. O There is a significant positive linear correlation…

Q1]. [30%] The data for this question given in the table below come from the sample data folder of MINITAB. The data give you the monthly number of people employed (in units of 1000) in the retail trade industry in the state of Wisconsin from January 1970 to December 1974. Copy this timeseries data into an excel spreadsheet in your home area first before you start your analysis. You can use Excel to answer this question. Month 1970 1971 1972 1973 1974 1 322 322 330 348 361 2 317 318 326 345 354 3 319 320 329 349 357 4 323 326 337 355 367 5 327 332 345 362 376 6 328 334 350 367 381 7 325 335 351 366 381 8 326 336 354 370 383 9 330 335 355 371 384 10 334 338 357 375 387 11 337 342 362 380 392 12 341 348 368 385 396 (a) (b) (c) (d) Draw a time series plot of the data and comment on any noticeable patterns. Describe an appropriate time series model for this data, giving your reasons, and obtain estimates of the components of your model using the decomposition method. Using your model…

Dr. Erie, a researcher at the Moore County Botany Institute, reported the following correlations. The correlation between pH level of soil and tomato weight is r₁ = -0.4. The correlation between hours of sunlight and tomato weight is r2 = 0.1. According to this information, which of the following statements are true? Select all that apply. Larger tomatoes result from more daily hours of sunlight. Larger tomatoes result from higher soil pH level. Tomato size is related more strongly to hours of sunlight than to soil pH level. None of these are true.

A statistician changes her level of significance from .001 to .05.  What impact will this change have on her risk of making a Type I and Type II error?  Is the change in the level of significance a good decision?  Why or why not?