Caroline Strömberg is a paleobotanist, studying of fossil plants. She hypothesized that some species of grass have evolved to accumulate silica in their tissues as a defense against herbivory.
1a. Describe how a species of grass "evolved to accumulate silica in their tissues," using Darwin's four postulates of natural selection (individual variation, heritability, over reproduction, the difference between generations).
1b. Describe the relative abundance and the relative position (in the sediment) of plant and animal fossils Dr. Strömberg is likely to observe if her observations support her hypothesis that "some species of grasses accumulate silica in their tissues as a defense against herbivory."
1c. Despite not being able to conduct experiments with fossils, many paleontologists conduct experiments using modern plant and animal species. Design an experiment (that you could conduct in modern-day) to test the hypothesis that the silica in some grass species is a defense against herbivory.
1d. Draw graphs showing the data you expect to see from your experiment. Label the axes, and include standard error bars.
In: Biology
QUESTION 2
Which causal criterion is established in an experiment by tests of statistical significance?
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a. |
association |
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b. |
direction of influence |
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c. |
nonspuriousness (elimination of rival explanations) |
7.7 points
QUESTION 3
What is the purpose of tests of statistical significance in an experiment?
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a. |
to establish direction of influence |
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b. |
to determine if random assignment created similar experimental and control groups |
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c. |
to determine the generalizability of the findings |
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d. |
to determine if chance is a reasonable explanation of experimental results |
7.7 points
QUESTION 4
In a laboratory experiment on helping behavior, two variables are manipulated: (1) others’ presence (whether participants worked alone on a task or with others) and (2) type of task (whether participants were timed or untimed on the task). The dependent measure is the number of seconds between someone’s request for help and the participant’s response. What type of experimental design is this?
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a. |
posttest-only control group design |
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b. |
pretest-posttest control group design |
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c. |
2 × 2 factorial design |
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d. |
2 × 4 factorial design |
7.7 points
QUESTION 5
In comparison with laboratory experiments, field experiments
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a. |
rarely involve manipulation of the independent variable. |
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b. |
afford less control over design and measurement. |
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c. |
are less likely to raise ethical issues. |
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d. |
usually involve more extensive debriefing |
In: Statistics and Probability
QUESTION 2
Which causal criterion is established in an experiment by tests of statistical significance?
|
a. |
association |
|
|
b. |
direction of influence |
|
|
c. |
nonspuriousness (elimination of rival explanations) |
7.7 points
QUESTION 3
What is the purpose of tests of statistical significance in an experiment?
|
a. |
to establish direction of influence |
|
|
b. |
to determine if random assignment created similar experimental and control groups |
|
|
c. |
to determine the generalizability of the findings |
|
|
d. |
to determine if chance is a reasonable explanation of experimental results |
7.7 points
QUESTION 4
In a laboratory experiment on helping behavior, two variables are manipulated: (1) others’ presence (whether participants worked alone on a task or with others) and (2) type of task (whether participants were timed or untimed on the task). The dependent measure is the number of seconds between someone’s request for help and the participant’s response. What type of experimental design is this?
|
a. |
posttest-only control group design |
|
|
b. |
pretest-posttest control group design |
|
|
c. |
2 × 2 factorial design |
|
|
d. |
2 × 4 factorial design |
7.7 points
QUESTION 5
In comparison with laboratory experiments, field experiments
|
a. |
rarely involve manipulation of the independent variable. |
|
|
b. |
afford less control over design and measurement. |
|
|
c. |
are less likely to raise ethical issues. |
|
|
d. |
usually involve more extensive debriefing |
In: Statistics and Probability
In North America many birds die because they collide with windows of high-rise buildings. One possible solution to resolve the problem is to construct windows angled down slightly toward the ground, so that they reflect the ground rather than an image of the sky to flying bird. An experiment compared the number of birds that died as a result of vertical windows, windows angled 20° of vertical and windows angled 40° off vertical. The angles were randomly assigned with equal probability to six windows and changed daily. Window shape, color and other external characteristics were kept identical. Window locations matched the same location characteristics in terms of ground and sky.
In: Statistics and Probability
An experiment was conducted to study growth characteristics of 8 different provenances (regions of natural occurrence) of Gmelina arborea (a tree native to southern Asia). There are three plots available for planting, so one tree of each provenance is planted in each plot. The response variable is the diameter of each tree (in centimeters) at breast height (1.4 meters above ground).
What type of design is being used in this experiment?
Perform the appropriate analysis to evaluate the differences in mean diameter at breast height of the eight provenances.
Which provenance(s), if any, has (have) largest mean diameter at breast height?
Comment on the effectiveness of the design in increasing the efficiency of the experiment.
| Provenance | Plot | Diameter |
| 1 | 1 | 30.85 |
| 1 | 2 | 38.01 |
| 1 | 3 | 35.1 |
| 2 | 1 | 30.24 |
| 2 | 2 | 28.43 |
| 2 | 3 | 35.93 |
| 3 | 1 | 30.94 |
| 3 | 2 | 31.64 |
| 3 | 3 | 34.95 |
| 4 | 1 | 29.89 |
| 4 | 2 | 29.12 |
| 4 | 3 | 36.75 |
| 5 | 1 | 21.52 |
| 5 | 2 | 24.07 |
| 5 | 3 | 20.76 |
| 6 | 1 | 25.38 |
| 6 | 2 | 32.14 |
| 6 | 3 | 32.19 |
| 7 | 1 | 22.89 |
| 7 | 2 | 19.66 |
| 7 | 3 | 26.92 |
| 8 | 1 | 29.44 |
| 8 | 2 | 24.95 |
| 8 | 3 | 37.99 |
In: Statistics and Probability
1.The Cu2+ ions in this experiment are produced by the reaction of 1.0g of copper turnings with excess nitric acid. How many moles of Cu2+ are produced?
2. Why isn't hydrochloric acid used in a direct reaction with copper to prepare the CuCl2 solution?
3. How many grams of metallic copper are required to react with the number of moles of Cu2+ calculated in Problem 1 to form the CuCl? The overall reaction can be taken to be: Cu2+(aq) + 2Cl-(aq) + Cu(s) -----> 2CuCl(s)
4. What is the maximum mass of CuCl that can be prepared from the reaction sequence of this experiment, using 1.0g of Cu turnings to prepare the Cu2+ solution?
5. A sample of the compound prepared in this experiment , weighing 0.1021g, is dissolved in HNO3, and diluted to a volume of 100 ml. A 10ml aliquot of that solution is mixed with 10 mL 6M NH3. The [Cu(Nh3)4]2+ in the resulting solution is found to be 5.16 x 10^-3 M.
a. How many moles of Cu were in the original sample, which had been effectively diluted to a volume of 200 mL.
b. How many grams of Cu were in the sample?
c. How many grams of Cl were in the sample? How many moles?
d. What is the formula of the copper chloride compound?
In: Chemistry
Anecdotal evidence has suggested that a specific type of oral contraceptive pill puts women at greater risk for blood clots. Researchers decide to examine this scientifically by starting a prospective cohort study. They enroll women between the ages of 15 and 45 who are using this type of oral contraceptive pill as well as similar women who are not using this contraceptive pill. At baseline none of the women had ever had a blood clot. Then they follow these study participants for 5 years, following up with them once a year to determine if they suffered from a blood clot. At the end of 5 years the researchers report the following information: Out of a total of 6000 women that were taking the oral contraceptive of interest, 575 had reported blood clots. Of the 7000 women not taking the oral contraceptive of interest, 250 reported a blood clot.
A. Create an appropriate 2x2 table for this data. (Fill out the chart)
| Blood Clot | No Blood Clot | |
| Oral Contraceptive (Exposure) | ||
| No Oral Contraceptive (No Exposure) |
B. Calculate the relative risk of having a blood clot for women taking the oral contraceptive pill in question compared to those not taking the contraceptive pill in question (show steps).
C. Assume that this RR is significant. What does this RR mean (be specific using the context of this study)?
In: Statistics and Probability
State Retirement Funding
A state retirement plan has been frozen. It is considered fully funded, with $635,244,352.26 of assets on hand and makes payouts to 1,000 recipients. It assumes it will earn 7.5% per year on these assets. The most recent total payout was $50,000,000. Next year it will be $51,000,000, which includes a 2% COLA increase in benefits. This payout amount is scheduled to increase by 2% per year for inflation. All interest earned and payments occur at the end of the year. For this cohort of retirees, the final payment will be made in exactly22 years from today. The fund balance at that time will be zero.
The effective rate for annuities like this is RATE = [(1+growth)/(1+Inflation)]-1=0.0539216.
The PV was calculated as =PV(RATE,22,-50000000,0,0)
A) Create an amortization table that shows the pension is fully funded.
B) Suppose that instead of 7.5% the assets earn 5% per year. By how much is the pension under-funded assuming the 2% COLA adjustment continues.
C) At a 5% growth rate what total annual payments can the original asset balance support for 22 years with no inflation adjustment? i.e., the same amount each year.
D) Given the initial balance of $635,244,352.26 and assuming a 2% COLA increase ever year, what initial payment can be made to beneficiaries?
In: Finance
| Use Excel to calculate the values to fill in the empty boxes. Feel free to add additional tables and calculations as | |||||||||||||
| needed. Please use the assignment 1 discussion board to ask questions. Once completed, save this file and | |||||||||||||
| upload it in Canvas. | |||||||||||||
| Historical Demand Data 2012 to 2016: | |||||||||||||
| The table reproduced below is the demand data for a company (aggregated) for the previous five years. | |||||||||||||
| 2012 | 2013 | 2014 | 2015 | 2016 | |||||||||
| Q1 | 11632 | 15034 | 16117 | 15565 | 16470 | ||||||||
| Q2 | 22509 | 26824 | 24169 | 20151 | 42858 | ||||||||
| Q3 | 21646 | 13314 | 14505 | 13392 | 19278 | ||||||||
| Q4 | 11355 | 10698 | 11176 | 10613 | 13934 | ||||||||
| Annual Demand | 67,142 | 65,870 | 65,967 | 59,721 | 92,540 | ||||||||
| Average Quarterly Demand | 16,785.50 | 16,467.50 | 16,491.75 | 14,930.25 | 23,135.00 | ||||||||
| Forecasting Using Moving Average Methods | |||||||||||||
| Using the historical demand data above, you are to determine the total annual demand forecast for 2016 and 2017 using: | |||||||||||||
| Ø the three-period moving average forecasting method | |||||||||||||
| Ø the three-period weighted moving average method with weights of .6, .3, and .1 | use .6 for most recent period | (Use .6 for most recent period) | |||||||||||
| Enter your forecast results in the following tables. | |||||||||||||
| 2016 Annual Forecast Using a Moving Average | 2016 Annual Forecast Using a Weighted Moving Average | 2017 Annual Forecast Using a Moving Average | 2017 Annual Forecast Using a Weighted Moving Average | ||||||||||
| 63,852.67 | 62209.7 | 72742.67 | 80037 | ||||||||||
| Calculate a Time Series Linear equation using the all of the above demand data: | |||||||||||||
| Using the historical demand data for 2012 through 2016, create a linear equation with the year as the independent variable and the annual volume as the dependent variable. | |||||||||||||
| (Excel can be quite useful here, consider the Slope and Intercept functions) | |||||||||||||
| Enter your linear equation in text from here: | y = a + bX | ||||||||||||
| Calculated 2017 Annual Forecast from Linear Equation: | x=2017 | ||||||||||||
| Forecasting Using an Exponential Smoothing Method and Seasonal Factors: | |||||||||||||
| Using the historical demand data for 2012 through 2016 given on the first page you are to: | |||||||||||||
| Ø Using the exponential smoothing forecasting method with an alpha value of 0.7, forecast the total annual demand for 2017. Start your forecast calculations with the total annual demand for 2012 and a starting forecast for 2012 that is the | |||||||||||||
| same as the 2012 total annual demand. After you have the annual forecast for 2012, use the average seasonal factors determined in the first part above to calculate the quarterly demand forecasts for 2017. | |||||||||||||
| Ø Determine the average seasonal factors for each quarter. Remember that you will first need to calculate the total annual demand and then average quarterly demand for each year of data as shown in lecture. | |||||||||||||
| Ø Determine the MAD, CFE, and MAPE errors between the annual forecast values using exponential smoothing for 2013 to 2016 and the actual annual demand data for 2013 to 2016. Enter the values in response to the three questions below. | |||||||||||||
| 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | ||||||||
| Actual Annual Demand | 67,142 | 65,870 | 65,967 | 59,721 | 92,540 | alpha= | 0.7 | ||||||
| Forecasted Annual Demand | 67,142 | 67142 | 66251.6 | 66052.38 | 61620.414 | ||||||||
| Forecast Error | -1,272 | -285 | -6,331 | 30,920 | |||||||||
| Values | Seasonal Factor for each Quarter | 2017 Quarterly Forecast | Index | 2012 | 2013 | 2014 | 2015 | 2016 | |||||
| Quarter 1 | 0.89 | Q1 | 0.69 | 0.90 | 0.96 | 0.93 | 0.98 | ||||||
| Quarter 2 | 1.63 | Q2 | 1.34 | 1.60 | 1.44 | 1.20 | 2.55 | ||||||
| Quarter 3 | 0.98 | Q3 | 1.29 | 0.79 | 0.86 | 0.80 | 1.15 | ||||||
| Quarter 4 | 0.69 | Q4 | 0.68 | 0.64 | 0.67 | 0.63 | 0.83 | ||||||
| Totals | 4.19 | ||||||||||||
| What is the MAD value for the exponential smoothing forecast? Answer = | |||||||||||||
| What is the CFE value for the exponential smoothing forecast? Answer = | |||||||||||||
| What is the MAPE value for the exponential smoothing forecast? Answer = | |||||||||||||
| Forecasting using trend with regression: | |||||||||||||
| Calculate forecasts for 2017, 2016 and 2015 using a linear regression of the previous three actual demand values. | |||||||||||||
| (Hint: You will need to calculate three different linear equations.) | |||||||||||||
| 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | ||||||||
| Actual Annual Demand | 67,142 | 65,870 | 65,967 | 59,721 | 92,540 | slope | -587.5 | ||||||
| Forecasted Annual Demand | 67,142 | 67,142 | 66,252 | 66,052 | 61,620 | intercept | |||||||
| Forecast Error | -6,331 | 30,920 | |||||||||||
| What is the MAD value for the trend with regression forecast? Answer = | |||||||||||||
| What is the CFE value for the trend with regression forecast? Answer = | |||||||||||||
| What is the MAPE value for the trend with regression forecast? Answer = | |||||||||||||
In: Operations Management
If all you had were the results from the RAND health insurance experiment, what policy recommendations would you make concerning the provision of health insurance? Explain. How certain would you be of your conclusions?
In: Economics