ii)What test would you do to find out if Drug B is more effective than A? justify your choice with explanation. write the equation that determines the test-statistic and define all the terms.

iii) if both drugs have the same active compound but with different weight, how do you show the effect of the active compound on the effectiveness of the drug?

what are the key parameters that indicate the validity of the model in Question (iii).

Exercise 12-4 Prepare a Statement of Cash Flows [LO12-1, LO12-2]

nStandard methodology for a single sample mean can be used to calculate a confidence interval for the slope of the least‑squares line and to test hypotheses other than H₀: ß₁= 0. In both cases, one needs to have an estimate of the slope and of its standard deviation (sometimes called standard error). Furthermore, one needs to recognize that the degrees of freedom for the standard deviation is the same as the error degrees of freedom (n ‑ 2).

Note that the EXCEL gives the standard error of estimate directly, but correctly calls it the standard deviation of the slope. Therefore, you must not divide by the square root of sample sizeas in example 16.

Use the above information to calculate a 90% confidence interval for the slope of the true regression line. For 30 degrees of freedom and a= 0.1, the critical t‑value is 1.697.

       (i.e. slope + margin of error)

nUse this same information to calculate a statistic to test the null hypothesis H₀: ß₁= 0.05 against a one‑sided alternative H₁: ß₁> 0.05. Use a 1 percent significance level (for which the critical value is 2.423).

            Reminder:  t = estimated value - hypothesized value  =   slope -  0.05

                                    standard error (deviation) of estimate        st dev of slope

Perform a regression analysis to see if body fat percentage is a good predictor of BMI. Use Excel or statistical software and be sure to include the following:

a) A scatterplot.
b) A correlation measure r.
c) The graph of the regression line on the scatterplot
d) The regression equation.
e) A discussion that explains how well body fat works as a predictor of BMI

1. Use columns F and G for the Least-Squares line.

1. Use Excel to make a scatter plot of the dat
2. Adjust the values of the x and y axes so that the data is centered in the plot.
3. Put the trendline on your plot.
4. Put the equation of the trendline on your plot.
5. Put the R² value on your plot.
6. The R value is a measure of how well the data fits a line. What is R? Is R + or - ?
7. Make a screen shot of your final plot. How well do you think the data fits the line? (good fit, moderate fit, marginal fit, no fit)

2. A brand of mints come in various flavors. The company says that it makes the mints in the following proportions.

A bag bought at random has the following number of mints in it.

Determine whether this distribution is consistent with company’s stated proportions.

1. What is the null hypothesis?
2. What is the alternative hypothesis?
3. Enter the observed number of times a flavor comes up in your test bag and the expected number of times that the flavor should come up into the X-squared goodness of fit applet.
4. What is the number of degrees of freedom?
5. What is the p-value? Provide a screen shot of your answer.
6. Using a 95% confidence interval, should you accept or reject the null hypothesis?
7. Does the distribution of flavors in your random bag support or contest the company’s state proportions? (yes or no).

3. This problem is the check to see whether you understand the X-squared test. There are only 2 test columns, so you cannot use the X-squared Goodness of Fit applet from the previous problem as it requires 3 or more test intervals.

You are told that a genetics theory says the ratio of tall:short plants is 3:1. You test this claim by growing 200 plants. You obtain 160 tall plants and 40 short plants. Using a X-squared test, determine whether or not your results supports the tall:short = 3:1 claim.

1. What is the null hypothesis for this test?
2. What is the alternative hypothesis?
3. Fill in the following table.

4. What is the value of X² for this data?
5. What is the number of degrees of freedom?
6. Use the X² calculator to compute p (use the right tail option). Provide a screen shot of your calculation.
7. Does this value of p support the null hypothesis at the 10% significance level? (yes or no and explain using your numbers)

1. Calculate Pearson’s r correlation coefficient?

2. What does the result you obtain say? (10points)

Now calculate whether the result obtained is statistically significant

3. State the Null and Alternative hypotheses? (5 points)

4. Establish your decision criteria and determine t critical (df = N-2).

5. Determine t calculated?

6. Are the results significant at the 5% level and explain what it means in terms of the Null hypothesis? (20 points)

E19-4. (Three Differences, Compute Taxable Income, Entry for Taxes) (LO 1, 2) Zurich Company reports pretax financial income of $70,000 for 2017. The following items cause taxable income to be different than pretax financial income. 1.Depreciation on the tax return is greater than depreciation on the income statement by $16,000. 2.Rent collected on the tax return is greater than rent recognized on the income statement by $22,000. 3.Fines for pollution appear as an expense of $11,000 on the income statement. Zurich's tax rate is 30% for all years, and the company expects to report taxable income in all future years. There are no deferred taxes at the beginning of 2017

. Instructions

(a) Compute taxable income and income taxes payable for 2017.

(b) Prepare the journal entry to record income tax expense, deferred income taxes, and income taxes payable for 2017.

(c) Prepare the income tax expense section of the income statement for 2017, beginning with the line “Income before income taxes.”