1.1. Find the second-order derivative of the following function:f(x) = log2cosx+ lnπ
2. Using limit definition of derivative, determine whether
definition of derivative, determine whether
1/2(t+ 5),0< t≤1 is differentiable at t= 1
√t+ 2, t >1
3. FM Corporation is a company that manufactures face masks. For everyxthousand pieces of facemask sold, the company’s revenue (in thousand pesos) isR(x) =x(5 +x),x≥0.
a. If the company soldxthousand pieces of face mask, find the company’s marginal revenue.Note: Marginal revenue is the increase in revenue that results from the sale of one additionalunit of output.
b. Find the number of face mask sold if the company has a marginal revenue of Php 25,000.
2.Show that the following compound propositions are logically equivalent using truth table.
¬p → (q → r) and q → (p
q → r) and q → (p ∨ r).
Q2. Write the converse, inverse and contrapositive of the statement:
“I will score marks whenever I will study”.
Q3. Convert the following compound propositions into English sentences for given
p: It is below freezing.
q: It is snowing.
(i) ¬q → ¬p
(ii) ¬q ∨ (¬p ∧ q )
(iii) p ↔ ¬q
(iv) p ∨ q
(v) ¬q ∧ ¬p
Q4. Determine whether each of the statements is true or false.
(i) If 1 + 1 = 2, then 2 + 2 = 5.
(ii) If 1 + 1 = 3, then 2 + 2 = 4.
(iii) If 1 + 1 = 3, then dogs can fly.
(iv) Monkeys can fly if and only if 1 + 1 = 3.
(v) A number is prime if and only if it is divisible by 1 and itself.
3.Can domestic dogs understand human body cues such as leaning? The experimenter leaned toward one of two objects and recorded
two objects and recorded whether or not the dog being tested correctly chose the object indicated. A four-year-old male beagle named Augie participated in this study. He chose the correct object 42 out of 70 times when the experimenter leaned towards the correct object.
(a) (2 points) Let the parameter of interest, π, represent the probability that the long-run probability that Augie chooses correctly. Researches are interested to see if Augie understands human body cues (better than gussing).
Fill in the blanks for the null and alternative hypotheses.
H0 : Ha :
(b) (6 points) Based on the above context, conduct a test of significance to determine the p-value to investigate if domestic dogs understand human body cues. What conclusion will you draw with significance level of 10%? (If you use an applet, please specify which applet you use, and the inputs.)
(c) (5 points) Based on the above context, conduct a test of significance to determine the p-value to investigate if domestic dogs understand human body cues. What conclusion will you draw with significance level of 5%? (If you use an applet, please specify which applet you use, and the inputs.)
(d) (2 points) Are your conclusions from part (b) and (c) the same? If they are different, please provide an explanation.
(e) (5 points) Shown below is a dotplot from a simulation of 100 sample proportions under the assump- tion that the long-run probability that Augie chooses correct is 0.50. Based on this dotplot, would a 90% confidence interval for π contain the value 0.5? Explain your answer.
(f) (4 points) Compute the standard error of the sample proportion of times that Augie chose the object correctly.
(g) (5 points)
(h) (3 points) question?
(i) (4 points)
(j) (4 points) A.
Construct an approximate 95% confidence interval for π using the 2SD method. What is the margin of error of the confidence interval that you found in the previous
How would you interpret the confidence interval that you found in part (g)?
Which of the following is a correct interpretation of the 95% confidence level?
If Augie repeats this process many times, then about 95% of the intervals produced will capture the true proportion of times of choosing the correct objective.
About 95% times Augie chooses the correct objective.
If Augie repeats this process and constructs 20 intervals from separate independent sam- ples, we can expect about 19 of those intervals to contain the true proportion Augie chooses the correct objective.
(k) (4 points)
object 21 out of 35 times.
Conjecture how, if at all, the center and the width of a 99% confidence interval would change with these data, compared to the original 2SD 95% confidence interval.
The center of the confidence interval would . The width of the confidence interval would .
(l) (4 points) Suppose that we repeated the same study with Augie, and this time he chose the correct object 17 out of 35 times, and we also change the confidence level from 95% to 99%. Conjecture how, if at all, the center and the width of a 99% confidence interval would change with these data, compared to the original 2SD 95% confidence interval.
Suppose that we repeated the same study with Augie, and this time he chose the correct
The center of the confidence interval would The width of the confidence interval would
4.Weight loss: In a study to determine whether counseling could help people lose weight, a sample of people experienced a
eople experienced a group-based behavioral intervention, which involved weekly meetings with a trained interventionist for a period of six months. The following data are the numbers of pounds lost for 14 people, based on means and standard deviations given in the article. Assume the population is approximately normal. Perform a hypothesis test to determine whether the mean weight loss is greater than 20 pounds. Use the =α0.10 level of significance and the critical value method.
22.5 28.5 7.6 24.1 21.5 12.9 17.3
21.2 37.6 33.8 12.1 36.3 24.1 19.4
You are asked to carry out a study on behalf of a business analytics specialised consultancy on a subsample
on a subsample of weekly data from Randall’s Supermarket, one of the biggest in the UK. Randall’s marketing management team wishes to identify trends and patterns in a sample of weekly data collected for a number of their loyalty cardholders during a 26-week period. The data includes information on the customers’ gender, age, shopping frequency per week and shopping basket price. Randall’s operates two different types of stores (convenient stores and superstores) but they also sell to customers via an online shopping platform. The collected data are from all three different types of stores. Finally, the data provides information on the consistency of the customer’s shopping basket regarding the type of products purchased. These can vary from value products, to brand as well as the supermarket’s own high-quality product series Randall’s Top. As a business analyst you are required to analyse those data, make any necessary modifications in order to determine whether for any single customer it is possible to predict the value of their shopping basket.
Randall’s marketing management team is only interested in identifying whether the spending of the potential customer will fall in one of three possible groups including:
• Low spender (shopping basket value of £25 or less)
• Medium Spender (shopping basket value between £25.01 and £70) and
• High spenders (shopping basket greater than £70)
For the purpose of your analysis you are provided with the data set Randall’s.xls. You have to decide, which method is appropriate to apply for the problem under consideration and undertake the necessary analysis. Once you have completed this analysis, write a report for the Randall’s marketing management team summarising your findings but also describing all necessary steps undertaken in the analysis. The manager is a competent business analyst himself/herself so the report can include technical terms, although you should not exceed five pages. Screenshots and supporting materials can be included in the appendix.
After completing your analysis, you should submit a report that consists of two parts. Part A being a non-technical summary of your findings and Part B a detailed report of the analysis undertaken with more details.
Part A: A short report for the Head of Randall’s Marketing Management (20 per cent). This should briefly explain the aim of the project, a clear summary and justification of the methods considered as well as an overview of the results.
Although, the Head of Randall’s Marketing Management team who will receive this summary is a competent business analytics practitioner, the majority of the other team members have little knowledge of statistical modelling and want to know nothing about the technical and statistical underpinning of the techniques used in this analysis. This report should be no more than two sides of A4 including graphs, tables, etc. In this report you should include all the objectives of this analysis, summary of data and results as well as your recommendations (if any).
Part B: A technical report on the various stages of the analysis (80 per cent).
The analysis should be carried out using the range of analytics tools discussed:
• SPSS Statistics
Ensure that the exercise references:
• Binary and multinomial logistic regression
• Linear vs Logistic regression
• Logit Model with odds Ratio
• Co-efficients and Chi Squared
• MLR co-efficients
• Assessing usefulness of MLR model
• Interpreting a model
• Assessing over-all model fit with Psuedo R-Squared measures
• Classification accuracy (Hit Ratio)
• Wald Statistic
• Odd ratio exp(B)
• Ratio of the probability of an event happening vs not happening
• Ratio of the odds after a unit change in the predictor to the original odds
• Residuals analysis
• Cook’s distance
• Adequacy (with variance inflation factor VIF and tolerance statistic)
• Outliers and influential points cannot just be removed. We need to check them (typo? – unusual data?)
• Check for multicollinearity
Write a short and concise report to explain the technical detail of what you have done for each step of the analysis.
The report should also cover the following information:
• Any type of analysis that might be useful and check whether the main assumptions behind the analyses do not hold or cannot be
• Give evidence of the understanding of the statistical tools that you are using. For example, comment on the model selection procedure and the coefficient interpretation, e.g. comment on the interpretation of the logistic regression coefficients if such a method is used and provide an example of
• Conclusions and explanation, in non-technical terms, of the main points