A new vaccination is being used in a laboratory experiment to investigate whether it is effective. There are 288 subjects in the study. Is there sufficient evidence to determine if vaccination and disease status are related?

Step 1 of 8: State the null and alternative hypothesis.

Step 2 of 8: Find the expected value for the number of subjects who are vaccinated and are diseased. Round your answer to one decimal place.

Step 3 of 8: Find the expected value for the number of subjects who are vaccinated and are not diseased. Round your answer to one decimal place.

Step 4 of 8: Find the value of the test statistic. Round your answer to three decimal places.

Step 5 of 8: Find the degrees of freedom associated with the test statistic for this problem.

Step 6 of 8: Find the critical value of the test at the 0.025 level of significance. Round your answer to three decimal places.

Step 7 of 8: Make the decision to reject or fail to reject the null hypothesis at the 0.025 level of significance.

Step 8 of 8: State the conclusion of the hypothesis test at the 0.025 level of significance.

an experiment was preformed under identical conditions as yours. The absorbance of the penny solution was recorded as 0.231 absorbance units. A calibration plot of absorbance vs concentration of cu(II) (mM) yielded the following trendily equations y= 11591x +.50

a. What is the concentration of the original penny solution?

b. How many grams of Cu are in this solution?

c. if the percent Cu was determined to be 2.70 percent what was the mass of the penny?

This is for my biochemistry lab, the experiment is dealing with trypsin and BPTI. I need to make a graph: plot the absorbance change per minute versus the BPTI concentration for each cuvette.

here are my cuvettes and amount of BPTI added to each

1- 0uL BPTI added

2- 10 uL BPTI added

3-20 uL BPTI added

4-30 uL BPTI added

5- 40 uL BPTI added

6- 50 uL BPTI added.

Each cuvette has a different amount of water and trypsin added to them, for a total volume of 100 uL in each cuvette.

I was give a sample of BPTI for which I had to find the concentration. Using the absorbance, I calculated that the concentration was 0.035mM. We then had to dilute this 10-fold, so to 0.0035mM.

I am not sure how to find the concentration of BPTI in each cuvette with this information. The molecular weight of BPTI is 6500. I don't know if that is needed.

I feel like this should be really easy, but I am having trouble doing this.

In an experiment involving the breaking strength of a certain type of thread used in personal flotation devices, one batch of thread was subjected to a heat treatment for 60 seconds and another batch was treated for 120 seconds. The breaking strengths (in N) of ten threads in each batch were measured. The results were

60 seconds:     43    52    52    58    49    52    41    52    56    58
120 seconds:   59    55    59    66    62    55    57    66    66    51

Let μXμX represent the population mean for threads treated for 120 seconds and let μYμY represent the population mean for threads treated for 60 seconds. Find a 99% confidence interval for the difference μX−μYμX−μY . Round down the degrees of freedom to the nearest integer and round the answers to three decimal places.

The 99% confidence interval is

An experiment is performed to study the fatigue performance of a high strength alloy. The number of cycles to crack initiation is measured for twenty specimens over a range of applied pseudo-stress amplitude (PSA) levels. Use the data in the table provided to fit the following three regression models with y = Cycles and x = PSA (note the natural log transform of y for all models):

PSA (x) Cycles (y)

80, 97379

80, 340084

80, 246163

80, 239348

100, 34346

100, 23834

100, 70423

100, 51851

120, 9139

120, 9487

120, 8094

120, 17956

140, 5640

140, 3338

140, 6170

140, 5608

160, 1723

160, 3525

160, 2655

160, 1732

i. A simple linear regression model: lny=β0+β1∙x .

ii. A quadratic polynomial model: lny=γ0+γ1∙x+γ2∙x2 .

iii. A simple linear regression model with a logarithm transformation on PSA: lny=δ0+δ1∙ln⁡(x) .

1. For model (i.), what hypothesis being tested by the ANOVA F-test (in terms of the coefficients of the model)? Interpret the conclusion of this test in the context of the engineering problem.
2. For model (ii.), what hypothesis being tested by the ANOVA F-test (in terms of the coefficients of the model)? Interpret the conclusion of this test in the context of the engineering problem.
3. What is the p-value for testing the significance of the quadratic term in model (ii.) (Ho: γ2=0)? Interpret the conclusion of this test in the context of the engineering problem.
4. Briefly discuss the advantages and disadvantages of each of the three models.

A factorial experiment was designed to test for any significant differences in the time needed to perform English to foreign language translations with two computerized language translators. Because the type of language translated was also considered a significant factor, translations were made with both systems for three different languages: Spanish, French, and German. Use the following data for translation time in hours.

Test for any significant differences due to language translator, type of language, and interaction. Use α = 0.05.

Find the value of the test statistic for language translator. (Round your answer to two decimal places.)

Find the p-value for language translator. (Round your answer to three decimal places.)

p-value =

State your conclusion about language translator.

Because the p-value > α = 0.05, language translator is not significant.Because the p-value ≤ α = 0.05, language translator is not significant.     Because the p-value ≤ α = 0.05, language translator is significant.Because the p-value > α = 0.05, language translator is significant.

Find the value of the test statistic for type of language. (Round your answer to two decimal places.)

Find the p-value for type of language. (Round your answer to three decimal places.)

p-value =

State your conclusion about type of language.

Because the p-value > α = 0.05, type of language is significant.Because the p-value ≤ α = 0.05, type of language is not significant.     Because the p-value > α = 0.05, type of language is not significant.Because the p-value ≤ α = 0.05, type of language is significant.

Find the value of the test statistic for interaction between language translator and type of language. (Round your answer to two decimal places.)

Find the p-value for interaction between language translator and type of language. (Round your answer to three decimal places.)

p-value =

State your conclusion about interaction between language translator and type of language.

Because the p-value > α = 0.05, interaction between language translator and type of language is not significant.Because the p-value ≤ α = 0.05, interaction between language translator and type of language is significant.     Because the p-value > α = 0.05, interaction between language translator and type of language is significant.Because the p-value ≤ α = 0.05, interaction between language translator and type of language is not significant.

An experiment is performed to study the fatigue performance of a high strength alloy. The number of cycles to crack initiation is measured for twenty specimens over a range of applied pseudo-stress amplitude (PSA) levels. Use the data in the table provided to fit the following three regression models with y = Cycles and x = PSA (note the natural log transform of y for all models):

PSA (x) Cycles (y)

80, 97379

80, 340084

80, 246163

80, 239348

100, 34346

100, 23834

100, 70423

100, 51851

120, 9139

120, 9487

120, 8094

120, 17956

140, 5640

140, 3338

140, 6170

140, 5608

160,    1723

160, 3525

160, 2655

160, 1732

i. A simple linear regression model: lny=β0+β1∙x .

ii. A quadratic polynomial model: lny=γ0+γ1∙x+γ2∙x2 .

iii. A simple linear regression model with a logarithm transformation on PSA: lny=δ0+δ1∙ln⁡(x) .

1. 1. For model (i.), what hypothesis being tested by the ANOVA F-test (in terms of the coefficients of the model)? Interpret the conclusion of this test in the context of the engineering problem.
2. 2. For model (ii.), what hypothesis being tested by the ANOVA F-test (in terms of the coefficients of the model)? Interpret the conclusion of this test in the context of the engineering problem.
   3. What is the p-value for testing the significance of the quadratic term in model (ii.) (Ho: γ2=0)? Interpret the conclusion of this test in the context of the engineering problem.
3. 4. Briefly discuss the advantages and disadvantages of each of the three models.

A manager of a large chain of appliance stores is planning to conduct a pricing experiment to determine the price elasticity of the chain’s line of microwave ovens. He plans to measure sales for the month of February, raise prices by 12 percent on March 15, and then measure sales for the month of April. Each sales measurement would take into account the total monthly microwave oven sales for all of the stores in his chain.

He has asked you to evaluate his research procedure and suggest any improvements. What would you suggest?

Fertilizer: In an agricultural experiment, the effects of two fertilizers on the production of oranges were measured. Twelve randomly selected plots of land were treated with fertilizer A, and

7

randomly selected plots were treated with fertilizer B. The number of pounds of harvested fruit was measured from each plot. Following are the results.

Part 1 of 3

Your Answer is correct

(a) Explain why it is necessary to check whether the populations are approximately normal before constructing a confidence interval.

Since the sample size is ▼small, it is necessary to check that the populations are approximately normal.

Part 2 of 3

Your Answer is correct

(b) Following are boxplots of these data. Is it reasonable to assume that the populations are approximately normal?

400

420

440

460

480

500

520

540

360

370

380

390

400

410

420

It ▼is reasonable to assume that the populations are approximately normal.

Part: 2 / 3

2 of 3 Parts Complete

Part 3 of 3

(c) Construct an

80%

confidence interval for the difference between the mean yields for the two types of fertilizer. Let

μ1

denote the mean yield for fertilizer A. Use tables to find the critical value and round the answer to one decimal place.

1. In this experiment we injected the sample to be analyzed by Gas Chromatograph equipped with an FID (Flame Ionization Detector). The detector ionizes the sample as it reaches it, and the peak is proportional to the number of ions with a live flame. Explain in detail, why we need to run a standard when the GC is equipped with an FID detector to identify the component in a sample?

2. Gas Chromatography is another chromatography method you are learning about this semester. Construct a table comparing and contrasting the two other chromatography methods (TLC and LC). Point out to differences and similarities between each method and the GC method. Give at least 4 criteria of comparison.

3. During last week’s lab of distillation, many of you had difficulties getting the fractional distillation column to work properly. Specifically, the liquid started condensing at the top of the fractional column and the vapor did not distill over. The lab instructor suggested that changing the bead size (which was in the column) to a larger size might improve the outcome of the experiment. Is this suggestion valid? Would it help the process or is this suggestion wrong? Explain your answer.