Given a 2-asset portfolio and two vectors of asset returns r and y, with volatility 15% and 30% respectively and correlation 0.70, determine the lincar combinations of a and y (principal components) that contribute to the overall variability of the portfolio.

What are the limitations of a simple linear correlation model that has only one independent variable?

The weights​ (in pounds) of 6 vehicles and the variability of their braking distances​ (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry​ surface? Use α=0.01. ​Weight, x 5930 5390 6500 5100 5890 4800   Variability in braking​ distance, y 1.76 1.95 1.91 1.62 1.64 1.50   1.Setup the hypothesis for the test. Choose a or b H0​: ρ ▼  A.less than or equals≤ b.less than< c.equals= d.greater than> e.greater than or equals≥ f.not equals≠ 0 Ha​: ρ ▼ A. less than or equals≤ b.less than< c.equals= d.greater than> e.greater than or equals≥ f.not equals≠ 0   2.Identify the critical​ value(s). Select the correct choice below and fill in any answer boxes within your choice. ​(Round to three decimal places as​ needed.) Fill the correct answer A. The critical value is=   B. The critical…

The weights​ (in pounds) of 6 vehicles and the variability of their braking distances​ (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry​ surface? Use α=0.05.   ​Weight, x 5910 5390 6500 5100 5820 4800   Variability in braking​ distance, y 1.78 1.94 1.88 1.58 1.64 1.50

The weights​ (in pounds) of 6 vehicles and the variability of their braking distances​ (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry​ surface? Use α=0.01. ​Weight, x 5940 5390 6500 5100 5820 4800   Variability in braking​ distance, y 1.71 1.94 1.87 1.65 1.61 1.50 LOADING... Click here to view a table of critical values for​ Student's t-distribution.   H0​:ρ_____0 Ha​:ρ____0 Identify the critical​ value(s). Select the correct choice below and fill in any answer boxes within your choice. ​(Round to three decimal places as​ needed.)   A. The critical value is _____.   B. The critical values are −t0=_____and t0=_____ Calculate the test statistic.   t= ​(Round to three decimal places as​ needed.) What is your​ conclusion?   There________enough evidence at the 5​% level of…

The weights​ (in pounds) of 6 vehicles and the variability of their braking distances​ (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry​ surface? Use α=0.05. ​Weight, x 5930 5330 6500 5100 5810 4800   Variability in braking​ distance, y 1.77 1.95 1.86 1.57 1.61 1.50   Setup the hypothesis for the test. choose H0​: ρ ▼  less than or equals≤ less than< equals= greater than> greater than or equals≥ not equals≠ 0 Ha​: ρ ▼  less than or equals≤ less than< equals= greater than> greater than or equals≥ not equals≠ 0 Identify the critical​ value(s). Select the correct choice below and fill in any answer boxes within your choice. ​(Round to three decimal places as​ needed.)   A. The critical value is nothing.   B. The critical values are −t0= and t0=.   Calculate the test…

Dr. Rancur believes that working memory errors (M) will increase linearly with increases in level of cognitive load (CL). He randomly assigns 10 subjects to each of four cognitive load levels: 1, 3, 5 and 7 and assesses subjects’ memory performance. The results of his study are exhaustively summarized below. Using the summary information, carry out an analysis to determine if M is related to CL in the way Dr. Rancur predicts. Make sure you estimate the magnitude of the association, and construct a confidence interval as appropriate. In a summary sentence or two, evaluate the evidence for or against Dr. Rancur’s prediction.

The weights​ (in pounds) of 6 vehicles and the variability of their braking distances​ (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry​ surface? Use α=0.01. ​Weight, x 5980 5340 6500 5100 5860 4800   Variability in braking​ distance, y 1.76 1.98 1.92 1.61 1.66 1.50 Setup the hypothesis for the test. Find the crtical value Find the test statistic Interpret the claim

This data table contians the listed prices and weights of the diamonds in 48 rings offered for sale in The Singapore Times. The prices are in Singapore dollars, with the weights in crats. Estimate the linear regression using weight as the explanatory variable and price as the response variable using the following two methods: a) Create a scatterplot with trendline (be sure to show the equation and R-square on the chart.) b) Use the Data Analysis Toolpak to do the work for you.

A zero correlation bewteen X and Y is least likely to occur if?