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business analytics and machine learning
Question 2: Machine Learning a) Which of unsupervised or supervised machine learning is best suited to assessing causatio ...
business analytics and machine learning
Question 2: Machine Learning a) Which of unsupervised or supervised machine learning is best suited to assessing causation? Explain your choice. b) Your analytics team presents you with two sets of results that have improved the organization’s ability to predict customer defections. The first method uses deep learning and has a precision of 85%. The second method uses decision trees and has a precision of 70%. The previous approach had a precision of 40%. i) Make a case for using the results of the deep learning method. ii) Make a case for using the decision tree method. In your answers, consider aspects of customer lifetime value and managerial decision making. c) An analytics team used two different models to predict the likelihood of an outcome. The results from two different analysts are below: Don’s Analysis Actual Positive Negative Predicted Positive 220 100 Negative 30 650 Katie’s Analysis Actual Positive Negative Predicted Positive 170 10 Negative 80 740 i) Use the Confusion Matrix and Index Calculation tables below to calculate the model performance measures. Confusion Matrix Actual Positive Negative Predicted Positive TP FP Negative FN TN Formula Don Calculation Katie Calculation Accuracy (completed as an example) (TP + TN) / (TP + TN + FP + FN) (220 + 650) / (220 + 650 + 100 + 30) 0.87 (170 + 740) / (170 + 740 + 10 + 80) 0.91 Precision TP / (TP + FP) Error rate (FP + FN) / (TP + TN + FP + FN) Recall TP / (TP + FN) Specificity TN / (TN + FP) False positive rate FP / (TN + FP) F-score 2* ((Precision*Recall) / (Precision + Recall)) ii) Describe a medical or business context where you would prefer to use Don’s model. Why do you prefer Don’s model? iii) Describe a medical or business context where you would prefer to use Katie’s model. Why do you prefer Katie’s model? Ian is an intern with the team who claims he made a breakthrough with a model that outperforms both Don’s and Katie’s. The confusion matrix for his model is below: Ian’s Analysis Actual Positive Negative Predicted Positive 249 2 Negative 1 748 iv) What could possibly have gone wrong that would result in his results being invalid? How could this be solved? (15 marks) Question 3: Experiments Jennifer was given the results of an experiment that was designed to determine if a 10% reduction in price on an online shopping portal would lead to an increase in purchases. Control and treatment group were created. These groups are described below: Control Group Treatment Group Number of males 25 25 Number of females 25 25 Average age 47 years 37 years Average spend per visit in the month BEFORE the experiment $25.00 $25.00 Average spend per visit in the month AFTER the experiment $25.00 $29.00 a) Were the control and treatment groups effectively randomized? Why or why not? b) What are the two most likely explanations for the treatment groups showing a higher average spend than the control group? c) What type of analysis could be used to remove one of the possible explanations for the difference in average spend? d) Experiments are useful in helping determine if people have responded due to a stimulus or if they would have responded even without the stimulus. Design an experiment that could demonstrate what proportion of people have responded to a stimulus. These people could be customers or employees within a company. Examples could be an advertising campaign to customers, or a policy of flexible work hours for employees. Requirements: i) How would you pick the treatment and control groups? Fill in the table below to indicate the number of people and 3 important characteristics that describe each group Control Group Treatment Group People Characteristic 1: Characteristic 2: Characteristic 3: ii) Predict the results and state the managerial conclusion you could make from this result. Use the table below to indicate the change in behavior you expect to observe. Control Group Treatment Group Observed behavior before treatment: Observed behavior after treatment: iii) State the managerial action you could take from the results of your experiment. Briefly describe a useful follow-up experiment that would further deepen understanding of why people behaved in the manner observed.
3 pages
Compounding Interest
You wish to invest in a 401-k. Calculate the future value of a presently valued $500 investment at the end of 10 years com ...
Compounding Interest
You wish to invest in a 401-k. Calculate the future value of a presently valued $500 investment at the end of 10 years compounding at a rate of 10.5%.
Boston University Project Risk Management Questions
Read pages 94 -101 of your textbook (Meredith et al., 2017), and discuss the following questions:How can project risk mana ...
Boston University Project Risk Management Questions
Read pages 94 -101 of your textbook (Meredith et al., 2017), and discuss the following questions:How can project risk management help improve project performance? What do you think are the most important factors to keep in mind and manage when managing risk? Some examples might be communication, schedule management, stakeholder management, etc. What will you be focusing on to manage the risks that you identify in your project?Reminder: A high quality post is one that explicitly links your personal opinion or experience with the course materials or other relevant outside sources using proper APA format. Both in-text citations and a reference list are required for this discussion. You may earn up to 10 points. You may find the following resources helpful.American Psychological Association. (2009). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.http://isites.harvard.edu/icb/icb.do?keyword=apa_e...https://prowritingaid.com/http://owl.excelsior.edu/grammar_essentials/http://www.apastyle.org/learn/tutorials/index.aspx
BUS 308 AU Descriptive Statistics Discussion
In a managerial report,Use descriptive statistics to summarize the data from Triple T’s study. Based on descriptive stat ...
BUS 308 AU Descriptive Statistics Discussion
In a managerial report,Use descriptive statistics to summarize the data from Triple T’s study. Based on descriptive statistics, what are your preliminary conclusions about whether the time spent by visitors to the Triple T website differs by background color or font? What are yourpreliminary conclusions about whether time spent by visitors to the Triple T website differs by different combinations of background color and font?Explain whether Triple T has used an observational study or a controlled experiment.Use the data from Triple T’s study to test the hypothesis that the time spent by visitors to the Triple T website is equal for the three background colors. Include both factors and their interaction in the ANOVA model, and use a=.05.Use the data from Triple T’s study to test the hypothesis that the time spent by visitors to the Triple T website is equal for the three fonts. Include both factors and their interaction in the ANOVA model, and use a=.05.Use the data from Triple T’s study to test the hypothesis that time spent by visitors to the Triple T website is equal for the nine combinations of background color and font. Include both factors and their interaction in the ANOVA model, and use a=.05.Discuss whether the results of your analysis of the data provide evidence that the time spent by visitors to the Triple T website differs by background color, font, or combination of background color and font. What is your recommendation?Step 3: Discuss:What recommendations does your ANOVA results support? Use findings from your managerial report to support your recommendations. Be sure to include why you support certain decisions over others. What surprising findings did you come up with during your analysis?
University of California Davis Data Wrangling HTML Document
below are the questions and i will attach the note if you need ---title: "Question 1"---
```{r, message = FALSE}library(t ...
University of California Davis Data Wrangling HTML Document
below are the questions and i will attach the note if you need ---title: "Question 1"---
```{r, message = FALSE}library(tidyverse)library(gapminder)# use ?gapminder get the desciption of the dataset `gapminder````
Consider the dataset `gapminder`.
##### (a) Modify the `continent` factor by classifying the Americas' countries into `South America` and `North America` Hint: the following countries are in South America. ```r c("Argentina", "Bolivia", "Brazil", "Chile", "Colombia", "Ecuador", "Paraguay", "Peru", "Trinidad and Tobago", "Uruguay", "Venezuela") ```
In the following questions, use the dataset modified in (a).
Hint: you could use `case_when` function.
##### (b) How many countries are there in the dataset? How about for each continent?
##### (c) For each year, which country had the largest gdp per capital?
##### (d) For each continent, which country experienced the sharpest increment rate in life expectancy from 1997 to 2007?
##### (e) Focus on the data in year 2007, what are the correlation coefficients between life expectancy and gdp per capital for each continent?
##### (f) Visualize part (e) by plotting gdp per capital vs life expectancy.---title: "Question 2"---
Consider the `flights` dataset in the package `nycflights13`.
```{r, message = FALSE}library(nycflights13)library(tidyverse)```
##### (a) Add a column that is the amount of time gained in the air (`gain = dep_delay - arr_delay`)
##### (b) Sort part (a) descedingly by the column you just created. Store the result as `flights_gain`.
##### (c) On average, did flights gain or lose time? (Hint: not average gain, but as percentage of positive gain.)
##### (d) On average, did flights heading to SeaTac ("SEA") gain or loose time?
##### (e) Summerize the mean, min and max of the `air_time` column for flights from `JFK` to `SEA`.
##### (f) In which month was the average departure delay the greatest?
##### (g) In which airport were the average arrival delays the highest?
#####(h) Which city was flown to with the highest average speed?
##### (i) Create a data frame of the average arrival delay for each destination, then use` left_join` to join on the `airports` dataframe, which has the airport info. (Hint: read the documentation of `airports` for the airport codes.)---title: "Question 3"---
##### (a) There is a csv file called `groceries.csv` in this directory. Read the csv file using `read_csv` from `tidyverse` and store the data frame as `groceries`. The datset shows the prices of some common groceries item in 4 different stores.
The table shows the prices of different items in 4 different stores.
##### (b) Is the data frame in wide format or long format?
##### (c) Try to convert it into the other format. Store it as `groceries2`.
##### (d) Use a randomized block design to analysis the store prices. Is there a store marking up the item prices?
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business analytics and machine learning
Question 2: Machine Learning a) Which of unsupervised or supervised machine learning is best suited to assessing causatio ...
business analytics and machine learning
Question 2: Machine Learning a) Which of unsupervised or supervised machine learning is best suited to assessing causation? Explain your choice. b) Your analytics team presents you with two sets of results that have improved the organization’s ability to predict customer defections. The first method uses deep learning and has a precision of 85%. The second method uses decision trees and has a precision of 70%. The previous approach had a precision of 40%. i) Make a case for using the results of the deep learning method. ii) Make a case for using the decision tree method. In your answers, consider aspects of customer lifetime value and managerial decision making. c) An analytics team used two different models to predict the likelihood of an outcome. The results from two different analysts are below: Don’s Analysis Actual Positive Negative Predicted Positive 220 100 Negative 30 650 Katie’s Analysis Actual Positive Negative Predicted Positive 170 10 Negative 80 740 i) Use the Confusion Matrix and Index Calculation tables below to calculate the model performance measures. Confusion Matrix Actual Positive Negative Predicted Positive TP FP Negative FN TN Formula Don Calculation Katie Calculation Accuracy (completed as an example) (TP + TN) / (TP + TN + FP + FN) (220 + 650) / (220 + 650 + 100 + 30) 0.87 (170 + 740) / (170 + 740 + 10 + 80) 0.91 Precision TP / (TP + FP) Error rate (FP + FN) / (TP + TN + FP + FN) Recall TP / (TP + FN) Specificity TN / (TN + FP) False positive rate FP / (TN + FP) F-score 2* ((Precision*Recall) / (Precision + Recall)) ii) Describe a medical or business context where you would prefer to use Don’s model. Why do you prefer Don’s model? iii) Describe a medical or business context where you would prefer to use Katie’s model. Why do you prefer Katie’s model? Ian is an intern with the team who claims he made a breakthrough with a model that outperforms both Don’s and Katie’s. The confusion matrix for his model is below: Ian’s Analysis Actual Positive Negative Predicted Positive 249 2 Negative 1 748 iv) What could possibly have gone wrong that would result in his results being invalid? How could this be solved? (15 marks) Question 3: Experiments Jennifer was given the results of an experiment that was designed to determine if a 10% reduction in price on an online shopping portal would lead to an increase in purchases. Control and treatment group were created. These groups are described below: Control Group Treatment Group Number of males 25 25 Number of females 25 25 Average age 47 years 37 years Average spend per visit in the month BEFORE the experiment $25.00 $25.00 Average spend per visit in the month AFTER the experiment $25.00 $29.00 a) Were the control and treatment groups effectively randomized? Why or why not? b) What are the two most likely explanations for the treatment groups showing a higher average spend than the control group? c) What type of analysis could be used to remove one of the possible explanations for the difference in average spend? d) Experiments are useful in helping determine if people have responded due to a stimulus or if they would have responded even without the stimulus. Design an experiment that could demonstrate what proportion of people have responded to a stimulus. These people could be customers or employees within a company. Examples could be an advertising campaign to customers, or a policy of flexible work hours for employees. Requirements: i) How would you pick the treatment and control groups? Fill in the table below to indicate the number of people and 3 important characteristics that describe each group Control Group Treatment Group People Characteristic 1: Characteristic 2: Characteristic 3: ii) Predict the results and state the managerial conclusion you could make from this result. Use the table below to indicate the change in behavior you expect to observe. Control Group Treatment Group Observed behavior before treatment: Observed behavior after treatment: iii) State the managerial action you could take from the results of your experiment. Briefly describe a useful follow-up experiment that would further deepen understanding of why people behaved in the manner observed.
3 pages
Compounding Interest
You wish to invest in a 401-k. Calculate the future value of a presently valued $500 investment at the end of 10 years com ...
Compounding Interest
You wish to invest in a 401-k. Calculate the future value of a presently valued $500 investment at the end of 10 years compounding at a rate of 10.5%.
Boston University Project Risk Management Questions
Read pages 94 -101 of your textbook (Meredith et al., 2017), and discuss the following questions:How can project risk mana ...
Boston University Project Risk Management Questions
Read pages 94 -101 of your textbook (Meredith et al., 2017), and discuss the following questions:How can project risk management help improve project performance? What do you think are the most important factors to keep in mind and manage when managing risk? Some examples might be communication, schedule management, stakeholder management, etc. What will you be focusing on to manage the risks that you identify in your project?Reminder: A high quality post is one that explicitly links your personal opinion or experience with the course materials or other relevant outside sources using proper APA format. Both in-text citations and a reference list are required for this discussion. You may earn up to 10 points. You may find the following resources helpful.American Psychological Association. (2009). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.http://isites.harvard.edu/icb/icb.do?keyword=apa_e...https://prowritingaid.com/http://owl.excelsior.edu/grammar_essentials/http://www.apastyle.org/learn/tutorials/index.aspx
BUS 308 AU Descriptive Statistics Discussion
In a managerial report,Use descriptive statistics to summarize the data from Triple T’s study. Based on descriptive stat ...
BUS 308 AU Descriptive Statistics Discussion
In a managerial report,Use descriptive statistics to summarize the data from Triple T’s study. Based on descriptive statistics, what are your preliminary conclusions about whether the time spent by visitors to the Triple T website differs by background color or font? What are yourpreliminary conclusions about whether time spent by visitors to the Triple T website differs by different combinations of background color and font?Explain whether Triple T has used an observational study or a controlled experiment.Use the data from Triple T’s study to test the hypothesis that the time spent by visitors to the Triple T website is equal for the three background colors. Include both factors and their interaction in the ANOVA model, and use a=.05.Use the data from Triple T’s study to test the hypothesis that the time spent by visitors to the Triple T website is equal for the three fonts. Include both factors and their interaction in the ANOVA model, and use a=.05.Use the data from Triple T’s study to test the hypothesis that time spent by visitors to the Triple T website is equal for the nine combinations of background color and font. Include both factors and their interaction in the ANOVA model, and use a=.05.Discuss whether the results of your analysis of the data provide evidence that the time spent by visitors to the Triple T website differs by background color, font, or combination of background color and font. What is your recommendation?Step 3: Discuss:What recommendations does your ANOVA results support? Use findings from your managerial report to support your recommendations. Be sure to include why you support certain decisions over others. What surprising findings did you come up with during your analysis?
University of California Davis Data Wrangling HTML Document
below are the questions and i will attach the note if you need ---title: "Question 1"---
```{r, message = FALSE}library(t ...
University of California Davis Data Wrangling HTML Document
below are the questions and i will attach the note if you need ---title: "Question 1"---
```{r, message = FALSE}library(tidyverse)library(gapminder)# use ?gapminder get the desciption of the dataset `gapminder````
Consider the dataset `gapminder`.
##### (a) Modify the `continent` factor by classifying the Americas' countries into `South America` and `North America` Hint: the following countries are in South America. ```r c("Argentina", "Bolivia", "Brazil", "Chile", "Colombia", "Ecuador", "Paraguay", "Peru", "Trinidad and Tobago", "Uruguay", "Venezuela") ```
In the following questions, use the dataset modified in (a).
Hint: you could use `case_when` function.
##### (b) How many countries are there in the dataset? How about for each continent?
##### (c) For each year, which country had the largest gdp per capital?
##### (d) For each continent, which country experienced the sharpest increment rate in life expectancy from 1997 to 2007?
##### (e) Focus on the data in year 2007, what are the correlation coefficients between life expectancy and gdp per capital for each continent?
##### (f) Visualize part (e) by plotting gdp per capital vs life expectancy.---title: "Question 2"---
Consider the `flights` dataset in the package `nycflights13`.
```{r, message = FALSE}library(nycflights13)library(tidyverse)```
##### (a) Add a column that is the amount of time gained in the air (`gain = dep_delay - arr_delay`)
##### (b) Sort part (a) descedingly by the column you just created. Store the result as `flights_gain`.
##### (c) On average, did flights gain or lose time? (Hint: not average gain, but as percentage of positive gain.)
##### (d) On average, did flights heading to SeaTac ("SEA") gain or loose time?
##### (e) Summerize the mean, min and max of the `air_time` column for flights from `JFK` to `SEA`.
##### (f) In which month was the average departure delay the greatest?
##### (g) In which airport were the average arrival delays the highest?
#####(h) Which city was flown to with the highest average speed?
##### (i) Create a data frame of the average arrival delay for each destination, then use` left_join` to join on the `airports` dataframe, which has the airport info. (Hint: read the documentation of `airports` for the airport codes.)---title: "Question 3"---
##### (a) There is a csv file called `groceries.csv` in this directory. Read the csv file using `read_csv` from `tidyverse` and store the data frame as `groceries`. The datset shows the prices of some common groceries item in 4 different stores.
The table shows the prices of different items in 4 different stores.
##### (b) Is the data frame in wide format or long format?
##### (c) Try to convert it into the other format. Store it as `groceries2`.
##### (d) Use a randomized block design to analysis the store prices. Is there a store marking up the item prices?
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