for anyone
QUESTION
Investors need to know how much risk they have to take to confidently expect a certain percentage return. Likewise, managers want to know what return shareholders require so that they can decide how to meet those expectations.
Respond to the following in a minimum of 175 words:
- Select 2–3 of the topics below and discuss how they each influence financial decisions regarding risk and return:
- The capital asset pricing model (CAPM)
- The constant–growth model
- Compute forward-looking expected return and risk
- Risk premiums
RESPOND TO THE FOLLOWING STUDENTS
David post
The 2 topics I have chosen is the Capital asset pricing model and the Compute forward-looking expected return and risk. To me these two models are the best way to help me determine which way to go in when purchasing or considering investments. The Forward-looking expected return and risk method has the advantage of your research and other advice to determine what kind of return you can reasonably expect which is important when investing as you are going to spend money so you want to make sure that you get it back and some extra. In addition, you will be investing time so you need effective tools to make sure you are using it well. The Capital asset pricing model determines if it is wise or even too risky to invest in a particular stock. Also determines not only if a stock is safe to invest in but also will I make money back and about how long am I looking at to make it back. The formula is Eri=RF+Bi (Erm-Rf) to use it effectively.
Gerardo post
The Capital Asset Pricing Model (CAPM) alludes to the connection between fundamental danger, particularly stocks, and expected to return on the resources. The CAPM is generally utilized for valuing the hazardous protections and for producing expected profits from resources because of the danger of such resources and capital expenses. The beta of a venture is a figuring of how much worth the speculation would bring to a market-like portfolio. At the point when a stock is more unstable when contrasted with the market, the beta will be higher than one. At the point when a stock has a beta worth short of what one, the equation suggests the danger of a portfolio is brought down. The constant growth model, or Gordon Growth Model, is a method of esteeming stock. It expects that an organization’s profits will keep on increasing at a steady development rate uncertainly. You can utilize that presumption to sort out what a reasonable value is to pay for the stock today dependent on those future profit installments. For an organization that delivers out a consistently rising profit, you can appraise the estimation of the stock with a recipe that accepts that continually developing payout is the thing that’s liable for the stock’s worth. You can utilize a numerical recipe called the steady development model, or Gordon Growth Model, to make this estimation or locate a stock valuation mini-computer apparatus on the web or in an advanced mobile phone application to do the calculation for you.
Keila post
Week 5 DISC.xlsx is uploaded below
To begin, there is a relationship between both variables with a str.xlsxong positive linear relationship. The regression model is y = 4.9216x – 25.168. Telling us that the slope is 4.921 significant and that every dollar spent in advertisement, equates in sales at a 4.921 rate. The intercept is -25.168 meaning that if money isn’t spend on advertisement then sales will decrease $25.168. The value of the regression coefficient is r=0.8237. Therefore r^2 is 0.6785. This tells us that 67.85% of sales is the dependent variable. The model underestimates spending in advertisement with $950,000. Sales would be $4,675,494.83.
Deonicia post
Data565_week _5_Discussion.xslx is uploaded below
Do you observe a relationship between both variables?
Yes, there is a linear relationship between both variables.
Use Excel to fit a linear regression line to the data. What is the fitted regression model?
The fitted regression model is y = 4.921x -25.168
What is the slope? What does the slope tell us? Is the slope significant?
The slope is 4.9216 and shows us an increase in advertisements it tends to increase sales as
well. Yes, it is significant.
What is the intercept? Is it meaningful?
The intercept is -25.168 and yes, it is meaningful since sales are affected.
What is the value of the regression coefficient, r?
r = 0.8237
What is the value of the coefficient of
determination, r^2?
r^2 = 0.6785
What does r^2 tell us?
r^2 tells us that a 67.85% variation in sales can be projected by the variation in the
advertisement.
Use the model to predict sales and the business spends $950,000 on the advertisement. Does
the model underestimates or overestimates sales?
Sales = -25.1682, Advertisement = 4.9216
-25.1682+4.9216(950) = 4650.3518 underestimated.
Sheet1
| Advertisement | Sales | ||||||
| 1068 | 4489 | ||||||
| 1026 | 5611 | ||||||
| 767 | 3290 | ||||||
| 885 | 4113 | ||||||
| 1156 | 4883 | ||||||
| 1146 | 5425 | ||||||
| 892 | 4414 | ||||||
| 938 | 5506 | SALES | |||||
| 769 | 3346 | ||||||
| 677 | 3673 | ||||||
| 1184 | 6542 | ||||||
| 1009 | 5088 | ||||||
| y = 4.9216x – 25.168 | |||||||
| 4675494.832 | |||||||
| ADVERTISEMENT |
Advertisement and Sales Revenues
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
ScatterPlot
Advertisement (
Sales (
068
26
Scatter Plot of Sales & Advertisement
Sales (
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Regression
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.8237332977 | |||||
| R Square | 0.6785365458 | |||||
| Adjusted R Square | 0.6463902004 | |||||
| Standard Error | 592.7335727464 | |||||
| Observations | 12 | |||||
| ANOVA | ||||||
| df | SS | MS | F | Significance F | ||
| 7415845.78405917 | 21.1077351717 | 0.0009886904 | ||||
| Residual | 3513330.8826075 | 351333.08826075 | ||||
| Total | 10929176.6666667 | |||||
| Coefficients | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% |
| 1042.2599271088 | -0.0241477518 | 0.9812098234 | -2347.4680715242 | 2297.1316033947 | ||
| X Variable 1 | 1.0712359149 | 4.5943155281 | 2.5347334365 | 7.30845816 |
000)
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
Sales ($’000)
1068
4489
1026
5611
767
3290
885
4113
1156
4883
1146
5425
892
4414
938
5506
769
3346
677
3673
1184
6542
1009
5088
Slope
4.9215957983
Intercept
-25.1682340648
Scatter Plot of Sales & Advertisement
Sales (
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
1068
4489
1026
5611
767
3290
885
4113
1156
4883
1146
5425
892
4414
938
5506
769
3346
677
3673
1184
6542
1009
5088
Slope
4.9215957983
Intercept
-25.1682340648
Scatter Plot of Sales & Advertisement
Sales (
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
Sales ($’000)
1068
4489
1026
5611
767
3290
885
4113
1156
4883
1146
5425
892
4414
938
5506
769
3346
677
3673
1184
6542
1009
5088
Slope
4.9215957983
Intercept
-25.1682340648
Scatter Plot of Sales & Advertisement
Sales (
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
Regression
000)
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
Sales ($’000)
1068
4489
1026
5611
767
3290
885
4113
1156
4883
1146
5425
892
4414
938
5506
769
3346
677
3673
1184
6542
1009
5088
Slope
4.9215957983
Intercept
-25.1682340648
Scatter Plot of Sales & Advertisement
Sales (
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
1068
4489
1026
5611
767
3290
885
4113
1156
4883
1146
5425
892
4414
938
5506
769
3346
677
3673
1184
6542
1009
5088
Slope
4.9215957983
Intercept
-25.1682340648
Scatter Plot of Sales & Advertisement
Sales (
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
Sales ($’000)
1068
4489
1026
5611
767
3290
885
4113
1156
4883
1146
5425
892
4414
938
5506
769
3346
677
3673
1184
6542
1009
5088
Slope
4.9215957983
Intercept
-25.1682340648
Scatter Plot of Sales & Advertisement
Sales (
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
000)
y = 4.9216x – 25.168
R² = 0.6785
1068 1026 767 885 1156 1146 892 938 769 677 1184 1009 4489 5611 3290 4113 4883 5425 4414 5506 3346 3673 6542 5088
Advertisement ($’000)
Sales ($’000)
Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8237332977
R Square
0.6785365458
Adjusted R Square
0.6463902004
Standard Error
592.7335727464
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
7415845.78405917
7415845.78405917
21.1077351717
0.0009886904
Residual
10
3513330.8826075
351333.08826075
Total
11
10929176.6666667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-25.1682340648
1042.2599271088
-0.0241477518
0.9812098234
-2347.4680715242
2297.1316033947
-2347.4680715242
2297.1316033947
X Variable 1
4.9215957983
1.0712359149
4.5943155281
0.0009886904
2.5347334365
7.30845816
2.5347334365
7.30845816
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