discussion Post. Plagarism checker will be used…Please see attachment. Script must be used

  • From the scenario for Katrina’s Candies, examine the procedure Herb will use to estimate the demand model developed in the scenario for Week 1. Analyze the elasticity of demand for products within the selected industry relevant to Katrina’s Candies. Determine the factors involved in making decisions about pricing these products that you believe to be the most influential.ECO550 Week 2 Scenario Script: Models of Supply and Demand, and Non-Price Determinants of Each
    Slide # Topics Narration
    Slide 1 Scene 1

    An older cottage style family run business (Katrina’s Candies)

     

     

    Slide 2 Scene 2

    Herb and Maria are in Herb’s office reviewing the demand model Herb and Renee formulated and discussing the data Maria compiled for estimating the model.

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

    Show the 5 variables on projector:

    Price of Katrina’s Sugar Free Chocolate;

    Price of the substitute good;

    Complementary good;

    Income;

    Number of buyers in the market.

     

     

     

     

     

     

     

     

     

     

     

     

    ECO550_2_2_Herb-1:Good day, Maria. Thanks for responding so quickly to my request for data.
    ECO550_2_2_Maria-1:Hello, Herb. No problem, I am assigned to the team to help with the data so when I received your email, I started looking for the data immediately.
    ECO550_2_2_Herb-2:Fantastic.  Let’s get started by reviewing the data you compiled. Then you can explain how I can use Excel to estimate the model.
    ECO550_2_2_Maria-2:First, would you review the model with me? I need to understand how the model is setup.
    ECO550_2_2_Herb-3:Oh, okay.  Recall from our team meeting that the team’s task is to provide Ken with information he canuse to respond to the Board of Directors’ request to expand Katrina’s into international markets.
    ECO550_2_2_Maria-3:Yes, I do recall that.
    ECO550_2_2_Herb-4: Renee and I met after the team meeting and decided the best way to proceed is to build a model of the demand for Katrina’s new sugar-free-chocolate candy then use the model to predict the demand.  In the model, the quantity of Katrina’s sugar-free-chocolate candy is the dependent variable and there are five independent variables.

     

    ECO550_2_2_Maria-4: Very interesting! Could you please go over these five independent variables with me?

     

    ECO550_2_2_Herb-5: Sure! The first independent variable is theprice of Katrina’s sugar-free chocolate.  The model must include the price of sugar-free-chocolate; otherwise, there is no demand curve.

     

    Next is the price of the substitute good.  In the case of chocolate, caffeinated coffee is the substitute good. Then there is the complementary good; for Katrina’s model, we selected bottled water; therefore the price of bottled water is the next independent variable. Income is another variable typically included in a demand curve.  For our model, we selected median household income.

     

    Last, since we are interested in the market for Katrina’s sugar-free-chocolate, the number of buyers in the market is included as an independent variable.

     

    ECO550_2_2_Maria-5: Thanks for going over that. You and Renee were certainly busy!
    ECO550_2_2_Herb-6: Yes, we were! I am going to use the data you provided to estimate the model to see if we selected the right set of determinants. Then, Renee and I can use the model to develop other measures that tell us more about the market for Katrina’s sugar-free-chocolate.

    Slide 3 Scene 3

    In Herb’s office to explain concept of estimation

    Shows the model on the projector

     

     

     

     

     

    Show on the projector: The notation on the left side of the equal sign, Qsubscript-d-k-s-f-c represents the dependent variable

    ECO550_2_3_Maria-1:You mentioned a lot of terms that are sort of new to me.  In this case what does “estimate” mean?

     

    ECO550_2_3_Herb-1:Here this may help with the concept of estimation, here’s what the finalized model should look like. We will talk about the actual estimation process in a few moments.

     

    ECO550_2_3_Maria-2: I’m not too sure of what this model contains, could you explain further?

     

    ECO550_2_3_Herb-2: Gladly! The notation on the left side of the equal sign, Qsubscript-d-k-s-f-c represents the dependent variable which is the quantity of Katrina’s sugar-free-chocolate candy.  The terms on the right-side of the equal sign are the independent variables I just explained.

     

    ECO550_2_3_Maria-3: That makes a lot more sense now! Where does the estimation process come into play?

     

    ECO550_2_3_Herb-3: Estimating the model means to find values for the coefficients, which in our model are the “b’s”.  Coefficients are numeric values that indicate how much the quantity of the dependent variable will change as independent variables change. The data you compiled will be used to calculate the coefficient values. This information is important as it helps determine the quantity demanded of sugar-free-chocolate changes in response to changes in the independent variables included in the model.
    ECO550_2_3_Maria-4:Okay, got it.  You’ve used the terms “dependent variable” and “independent variable”while explaining the model.Could you provide me with some insight on these terms?
    ECO550_2_3_Herb-4:Yes, of course.  A dependent variable is a variable that changes value when changes occur in some other variable. The term “variable” is used to capture the fact that the value can change.  On the flipside, an independent variable is a variable that impacts or causes a change in the dependent variable.
    ECO550_2_3_Maria-5:Okay, Herb, I think I understand now.  Can you give me examples of dependent and independent variables that are different from Katrina’s sugar-free-chocolate candy?
    ECO550_2_3_Herb-5:Yes I can, Maria.  I believe a good example isumbrellas. Think about umbrella sales, when the weather changes from clear to rainy, people buy more umbrellas. This is especially prevalent with people who may have left their umbrellas at home.  In this example, the quantity of umbrellaspurchased changes when the weather changes, so it is quite easy to identify the dependent and independent variables in this example.  The quantity of umbrellas sold is the dependent variable while rain is the independent variable.
    ECO550_2_3_Maria-6:That’s anexcellent example, Herb. I understand exactly how the model functions now.  The independent variables you and Renee selected will explain what caused or is causing the demand for Katrina’s sugar-free chocolate candy to change.
    ECO550_2_3_Herb-6: Yes, Maria, that’s right. Using the data you provided, we are going to see how well the model we formulated explains the demand for sugar-free-chocolate candy.

    Slide 4 Scene 4

    Herb’s office to go over the data Maria collected.

     

     

     

     

     

     

     

     

     

     

     

     

     

    Insert the URL for the Census Bureau

     

     

     

     

     

     

     

     

     

    Show a pictures of the Strayer Resource Center

     

     

    ECO550_2_4_Herb-1: Maria, could you update me on the data you collected for this project?

     

    ECO550_2_4_Maria-1:I located most of the data you requested involving the number of sugar-free-chocolates Katrina’s sold since introducing the new candy and the selling prices. You will notice that this data is available in our accounting database.  However, I had to search outside of the database for other data.
    ECO550_2_4_Herb-2: What other data did you need to acquire and how did you go about doing this search?

     

    ECO550_2_4_Maria-2:I needed to find the prices of coffee and I simply did a Google search and looked for reliable sources of information pertaining to concepts such as the price of coffee.

     

    ECO550_2_4_Herb-3: Okay, that leavesdata on the price of water, median income and the number of buyers.  Where did you retrieve data for these independent variables?
    ECO550_2_4_Maria-3:Well, I retrieved median household income data from the U.S. Census Bureau website. Census data is easy to find, reliable and easy to use.  I just went to the Census Bureau website, typed in the key term, “household income,” then selected the median household income for the appropriate years.
    ECO550_2_4_Herb-4:Did you know you could have retrieved data on income and other variables by going through Strayer’s Global campus’ Resources Center?
    ECO550_2_4_Maria-4:No, I didn’t know that, Herb.  Isn’t access to Strayer’s Resources Center restricted?
    ECO550_2_4_Herb-5:Yes, only enrolled students, faculty and staff and subscribers can use the Resource Center. However, since there are so many Strayer educated employees here at Katrina’s, we have free access to the Resource Center.
    ECO550_2_4_Maria-5:That’s great news, Herb!
    ECO550_2_4_Herb-6: I agree! So in the future when you need to search for data, check out the Resource Center first.

    Slide 5 Scene 5

    Herb’s office to go over the data Maria collected and investigate briefly the Resource Center

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

    ECO550_2_5_Maria-1:The Resource Center seems very easy to use. I’ll definitely use it next time I need to find data. Let’s see where we are with the data I compiled.  I told you about data for the quantity and price of Katrina’s sugar-free-chocolate candy, data on the price of caffeinated coffee and median household income. Now, I have to tell you, I had a problem finding price data for bottled water and finding the number of consumers who purchase chocolate candy.
    ECO550_2_5_Herb-1:Oh, no.  Does this mean we have to change our model?
    ECO550_2_5_Maria-2:That depends upon whether you accept the proxy variables I found and recommend using them to “estimate” the model.
    ECO550_2_5_Herb-2: I’m not quite sure about these “proxy variables.” Could you elaborate on this concept?

     

    ECO550_2_5_Maria-3: Sure thing! When data is not available for a variable, analysts often use data from another variable to capture the same relationship as the original variable. It is this substitute variable that is referred to as “proxy variable.”
    ECO550_2_5_Herb-3: Does data for proxy variables work as well when the model is estimated?
    ECO550_2_5_Maria-4:That depends, in some cases the answer is yes and in others it is no. In order to determine the answer, you are required to estimate the model to find out.  Keep in mind that if the proxy data does not work, then the variable is dropped from the model.
    ECO550_2_5_Herb-4: Thanks for the clarification on this subject. We can continue with our updates on the data you collected.

    Slide 6 Scene 6

    Herb’s office to go over the data Maria collected.

     

    Show data table of per capita consumption of bottled water.

    ECO550_2_6_Maria-1:Again, since I was unable to locate the price of bottled water, I had to add a proxy variable. The data I used dealt with the per capita consumption of bottled water.

     

    ECO550_2_6_Herb-1:The data I’m looking at shows the per capita consumption of bottled water, by gallon over twelve years.  I think this data works well as a proxy. What data did you find to proxy the number of buyers?

     

    ECO550_2_6_Maria-2:I had to think hard about a number of buyers proxy. Inthe end, I found a good proxy in a Department of Commerce report, it is called “Current Industrial Reports.” The proxy I used dealt with the confectionery exports of domestic merchandise measured in pounds per year.

     

    ECO550_2_6_Herb-2:This data also serves as a great proxy.  Of course, I’ll have to consult with Renee to get her opinion because she’s the one mentoring me on this project.  But I’m fairly certain Renee will agree with me.
    ECO550_2_6_Maria-3:Okay! Here’s the data I compiled from our accounting records.
    ECO550_2_6_Herb-3:Great!  Now, can we create the dataset in Excel and then estimate the model?

     

    ECO550_2_6_Maria-4:That’s correct! I have some great resources that will help you review how to create datasets in Excel and how to use Excel functions to estimate the model. Please look over these resources and I will get back to you once you are finished.

     

    ECO550_2_6_Herb-4:Okay that sounds great!

    Slide 7 Scene 7

    Interaction Slide

    Incorporate iPad to show Videos about Excel and model creation

    ·         Multiple Linear regression analysis using Microsoft Excel’s Data Analysis toolhttp://www.youtube.com/watch?v=ZwtxHXh-ZXU

    ·         Multiple Regression Interpretation in Excel http://www.youtube.com/watch?v=tlbdkgYz7FM

     

     

     

    Slide 8 Scene 8

    Herb’s office to go over the data Maria collected

     

     

    Show regression output table

     

     

     

     

     

    Herb shows formula

     

     

     

     

     

     

     

     

     

     

     

     

     

     

    Herb shows Maria the updated formula

     

     

     

     

     

     

    One more formula for Herb to go over

     

     

    Display on projector: The new quantity demanded is, three hundred seventy-four thousand, three hundred sixty-six point two boxes of sugar-free-chocolates.

    ECO550_2_8_Maria-1: I hope those videos helped you gain a better understanding of using Excel to create data sets. I want you to keep in mind that the procedure we will be using to estimate the model is regression. The model Renee and you formulated is a multiple regression model because there is more than the price of chocolate included as an independent variable. Take a look at the regression output for our estimated model.

     

    ECO550_2_8_Herb-1: Wow! That was fast!
    ECO550_2_8_Maria-2:Yes, Herb, Excel generates results almost instantaneously.
    ECO550_2_8_Herb-2: Okay, let’s see what we have. I see the coefficients are presented in a single column.  Let me rewrite the model to include the coefficient values.

     

    ECO550_2_8_Maria-3:What does all of this mean?
    ECO550_2_8_Herb-3: Well, the first number, three hundred and forty four thousand and four hundred point five refers to the number of boxes of sugar-free-chocolate demanded if none of the independent variables changedtheir value.  If we assume one of the other variables changes while all of the others remain constant, then we calculate a new number of boxes of chocolate.

     

    ECO550_2_8_Maria-4: Could you give me an example for this change?

     

    ECO550_2_8_Herb-4: Sure! For my example, let’s assume that the price of Katrina’s sugar-free-chocolates declines by one dollar while none of    the other independent variables changes.  According to our model, the decrease in price would cause quantity demanded to increase by twenty nine thousand and nine hundred and sixty five point seven boxes. Each of the other coefficients is then interpreted similarly. Here’s the way we calculate the change in quantity demanded, if price was to change.For all of the variables that are constant, that is, those unchanging variables, we substitute a “zero.”
    ECO550_2_8_Maria-5: That is very interesting! Is there anything else I should know?

    ECO550_2_8_Herb-5: There is one more thing I’d like to add. For the price of Katrina’s sugar-free-chocolate, substitute one dollar, with a negative sign in front of it to indicate price declined. Here, I’ll show you how the model determines quantity demanded. After making the changes the new quantity demanded is, three hundred seventy-four thousand, three hundred sixty-six point two boxes of sugar-free-chocolates.

     

    ECO550_2_8_Maria-6:Thank you for sharing that with me! Now that you explained this all to me, things are much clearer.
    ECO550_2_8_Herb-6: Not a problem at all. As you can see, regression models are useful but only if the results from the model are valid.

     

    Slide 9 Scene 9

    Herb’s office to conduct significance test on the model and coefficients with Maria

     

    Display on projector: The coefficient of determination ranges from 0 to 1.

    Display on projector: A higher adjusted R-square indicates a better model.

     

     

    http://wn.com/r-squared_or_coefficient_of_determination

     

    ECO550_2_9_Herb-1: Now we need to check the model and coefficients for significance.

     

    ECO550_2_9_Maria-1:How do we that?
    ECO550_2_9_Herb-2:First, we evaluate the adjusted R-square value to see how much of the variation in the quantity demanded of sugar-free-chocolates is explained by the independent variables we included in the model.  The closer R-square is to one, the better is the explanatory power of the independent variables.The adjusted R-square for our model’s results is point seven, nine, nine which means the model explains seventy-nine point nine percent of the variation in the quantity of sugar-free-chocolates. Maria, I found this video that helps to explain the coefficient of determination from another standpoint.
    ECO550_2_9_Maria-2:Based upon the explanation you gave about R-square being close to one, seventy-nine-point-nine percent is very good.
    ECO550_2_9_Herb-3:Yes, it looks as if we included the right set of independent variables.

     

    ECO550_2_9_Maria-3:What’s next, Herb?
    ECO550_2_9_Herb-4:Now we evaluate the overall significance of the independent variable. We are looking for the answer to the question: Can the behavior of the dependent variable, our quantity of sugar-free-chocolates, be explained without relying on the independent variables included in the model? For this test we will evaluate the F-statistic. We first need to state the level of significance, called the “critical-value,” which we will use to test the F-statistic.
    For our model we are going to use the five-percent level of significance; therefore,the table gives us a critical F-value of four-point-one-two.
    ECO550_2_9_Maria-4:I think I understand how you selected the critical value. I think now we must compare the F-statistic generated for the model to the critical value.

     

    ECO550_2_9_Herb-5:Yes, that’s exactly what we will do. Since the F-calculated value is eleven-point-nine-five-two and is greater than four-point-one-two, a significant relationship does exist between the quantity of sugar-free-chocolate and the four independent variables.
    ECO550_2_9_Maria-5:Great! So we’re done then?
    ECO550_2_9_Herb-6:No, not quite yet. We still need to evaluate the significance of each coefficient.  We can actually use the same method used to find the critical value of F only this time we will conduct a t-test on each coefficient value.

    Slide 10 Scene 10

    Herb’s office to conduct significance test on the model and coefficients with Maria

     

    ECO550_2_10_Maria-1:So based on the t-test, tell me which independent variables are significant.
    ECO550_2_10_Herb-1: According to the t-test, only the price per boxand bottled water are significant.  The coefficient on median income is marginally significant; however, we cannot use the coefficient for anything. Surprisingly, the caffeinated coffee coefficient is insignificant.
    ECO550_2_10_Maria-2:I see why you are saying coefficients are insignificant.
    ECO550_2_10_Herb-2: Yes, this revelation about independent variable significance means we need to drop the caffeinated coffee variable and re-estimate the model.
    ECO550_2_10_Maria-3:Is it okay to drop variables from a model after the model is estimated?
    ECO550_2_10_Herb-3: Yes, if an independent variable is not significant, one of the recommended solutions is to drop the variable from the model.  In our model, this means sugar-free-chocolate and caffeinated coffee are not substitute goods so coffee does not contribute anything to our understanding about demand for Katrina’s sugar-free-coffee.
    ECO550_2_10_Maria-4:Does dropping the insignificant variable mean we still use the coefficients generated when caffeinated coffee was a variable in the model?
    ECO550_2_10_Herb-4: That is a good question, Maria!  The answer is, no.  When we drop a variable like caffeinated coffee from the model, we have to re-estimate the model and then run the Excel regression procedure again to generate new coefficient values.

     

    ECO550_2_10_Maria-5:Let’s run the regression without data on caffeinated coffee—I’m anxious to see if there is any difference in the results.

     

    ECO550_2_10_Herb-5: Okay, but before we re-estimate the model, I think we should also drop the bottled water variable. After some consideration, the amount of water consumed is not a good proxy for the price of water.  Also, the correlation coefficient between bottles of water and income is nearly one.  Therefore, there seems to be a problem with their correlation.  Keep in mind that we also need to add a Dummy variable to measure the impact of sugar-free-chocolate which Katrina’s introduced into the market last year. Renee and I forgot to include a dummy variable in the first model.

     

    ECO550_2_10_Maria-6:Whatever you say, Herb.  You know this process better than I do.

     

    ECO550_2_10_Herb-6:Let me compute this quick. (pause) Here are the results now.

     

     

    Scene 11 Scene 11

    Herb’s office to conduct significance test on the model and coefficients with Maria

     

    ECO550_2_11_Maria-1:Are these results better, Herb?
    ECO550_2_11_Herb-1:Yes, everything is now significant. Now we can use the regression equation to derive decision-making statistics like elasticity coefficients.
    ECO550_2_11_Maria-2:How do we go about doing that?
    ECO550_2_11_Herb-2: Make a note that the point elasticity of demand is calculated as the change in quantity divided by the change in price times price divided by quantity.  Here’s how the formula looks.

     

    ECO550_2_11_Maria-3:Okay, so where is the data to calculate elasticity?
    ECO550_2_11_Herb-3:The regression coefficients or the b’s in the model are the change in quantity divided by a change in price, so that part is simple.
    ECO550_2_11_Maria-4:Do you mean the negative forty-two thousand, one hundred eighty-nine that is the coefficient for the price variable?
    ECO550_2_11_Herb-4:Yes, however, we have to calculate the “q” that’s in the elasticity of demand formula.

     

    ECO550_2_11_Maria-5:What does the ‘q” stand for?
    ECO550_2_11_Herb-5: In the elasticity formula, q, is the quantity demanded at a specific price.  For this step, we first find the demand curve.
    ECO550_2_11_Maria-6:I thought we already have the demand curve.
    ECO550_2_11_Herb-6: Not quite yet, I was discussing the regression equation which includes all of the independent variables we included in the model.  The demand curve is different, as only the price variable is included in the demand curve.For our example we will use some numbers from 2004. We will then use these numbers to showcase how to derive the demand curve. First, go back to the regression equation. Now substitute the data as follows. For income, substitute one-thousand dollars and for exports substitute two-six-three-three-six-six point seven.

     

    Slide 12 Scene 12

    Herb’s office to conduct significance test on the model and coefficients with Maria

     

    ECO550_2_12_Maria-1:Okay, I did that. What about the price variable, should I substitute for price?

     

    ECO550_2_12_Herb-1:No, not yet.  Just solve what you have as this will give the demand curve.

     

    ECO550_2_12_Maria-2:Is that all there is to finding the demand curve from the regression model?
    ECO550_2_12_Herb-2:Yes, that’s it!  We’re nearly finished as we have only two more steps to calculate elasticity. Again using the data from 2004 substitute twenty-four dollars for price variable into thedemand curve and solve to get a quantity equal to two-million, ninety-six thousand, seven-hundred eight-point eight-eight. The elasticity coefficient is then negative zero point four-eight-two-nine. We can then round to negative zero point four-eight-three.

     

    ECO550_2_12_Maria-3:Thank you for going over this with me. Since you showed me how to do this, things seem clearer. Does this mean we are finished with this stage of the process to create information for Ken to use when he considers the decision to expand into international markets?
    ECO550_2_12_Herb-3: Yes, we have completed this stage.  We just need to update Renee on our progress.

     

    ECO550_2_12_Maria-4: I will update Renee on our findings. While I complete this task could you complete this review activity based on what we just discussed?

     

    Slide 13 Scene 13

    Check Your Understanding

    Scenario-based and will use folder structure to present scenario, then have tabs to represent options for answers

    Narrations will be provided for scenario overview and choices (feedback included as well)

    ECO550_2_13_Maria-1: Based upon the result that the price elasticity of demand coefficient is -0.483 for Katrina’s sugar-free-chocolate, Herb can advise Ken that Katrina’s should never use price as a tool for increasing total revenue?

    ECO550_2_13_Maria, Agree, Response 1-2:Agreed, that’s correct since price elasticity of demand is less than one it means demand is elastic. As a result of this, the increasing price would lower total revenue because customers would react very strongly to an increase in price by changing their purchases by a greater percentage than the percentage change in price.  Therefore, Herb is giving Ken the appropriate advice.

     

    ECO550_2_13_Maria Incorrect response-3:

    We should expect the percentage change in quantity demanded to change by less than the percentage change in price.

     

    ECO550_2_13_Maria, Disagree, Option 2-4: When the absolute value of the price elasticity of demand coefficient is less than one it means demand is inelastic, so if price is increased by a certain percentage, say ten percent, demand will change by a lower percentage, such as eight percent.  Therefore, when demand is price inelastic, increasing the price actually results in higher total revenue. For Katrina’s, this means demand for sugar-free-chocolate is price inelastic and the company could increase total revenue by increasing price.

     

    Slide 14 Summary

    Concluding scene taking place in conference room

    ECO550_2_14_Herb-1:Maria, we have discussed and analyzeda lot today.

     

    ECO550_2_14_Maria-1:We sure have.  Let’s outline the tasks we completed to make certain we remember everything. First,you explained the demand model that you and Renee formulated.I then described the data and its sources for the data that I compiled.  We later discussed proxy data and agreed it was okay to use this kind of data for two of the variables.
    ECO550_2_14_Herb-2: Let’s not forget about our creation of the data set in Excel along with the creation of our estimation model.

     

    ECO550_2_14_Maria-2: I’m glad you brought that up! Next, we discussed the results of our model and conducted significance tests for the model and its coefficients. Lastly, we used these results to generatean amended regression model to formulate the demand curve and calculate the price elasticity of demand coefficient.
    ECO550_2_14_Herb-3: Thank you for the recap. I will be sure to update Renee on what we covered today.

     

    ECO550_2_14_Maria-3:That’s fantastic! I will talk to her as well when I see her next.  That is all for today, I’m now going back to my office to update Ken on our progress with this project. Until we meet again,don’t forget to complete your weekly threaded discussions based on the key concepts we covered this week.

     

    ECO550_2_14_Herb-4:Thanks,Maria and have a great day!

     

     

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