Researcher, Harry, is using 100 portfolios of companies listed on US stock exchanges to investigate the Capital Asset Pricing Model.

The 100 portfolios have been created to have significant differences in their firm size and in the ratio of their book value to their market value.

They have been formed by ranking the companies twice. First the companies are ranked from smallest to largest market capitalisation (size). Next the companies are ranked from lowest to highest by the ratio of their book value to their market value (valuation ratio).

10 sets of companies have been formed by splitting the size ranked companies at appropriate cutoffs. These 5 sets of companies have names: ME1, ME2, ME3, ME4, ... ,ME10 with ME1 being the name of the set of smallest companies and ME10 being the name of the set of largest companies.

10 sets of companies have been formed by splitting the valuation-ratio ranked companies at appropriate cutoffs. These 10 sets of companies have names: BM1, BM2, BM3, BM4, ..., BM10 with BM1 being the name of the set of lowest valuation ratio companies and BM10 being the name of the set of highest valuation ratio companies.

The 100 portfolios are formed as the intersections of each of these size and valuation ratio sets. For example, the portfolio of smallest companies with highest valuation ratios is formed from the companies that are in the set "M1" and in the set "BM10". That portfolio is named "ME1 BM10". This naming convention applies to all 100 portfolios.

This portfolio formation process is redone each year.

The researcher uses 5 years of monthly data on portfolio rates of return, the risk-free rate of return, and a proxy for the market rate of return to estimate the CAPM Beta for each of the 100 portfolios.

Using the output from these time-series CAPM regressions, she creates this cross-section dataset to investigate the role of the CAPM Beta estimates in explaining the cross-section variation in portfolio rates of return across the 100 portfolios.

The dataset contains the following variables:

• alpha: The intercept estimate from the CAPM regression for each portfolio;
• beta: The intercept estimate from the CAPM regression for each portfolio;
• rsquared: The R-squared from the CAPM regression for each portfolio;
• residualStandardError: The estimate of the standard error of the residuals from the CAPM regression for each portfolio;
• portfolioInSampleAverageReturn: The average rate of return across the 5 years of monthly data used to estimate the CAPM regression, calculated for each portfolio;
• portfolioFutureSampleAverageReturn: The average rate of return across the 1 year of monthly data immediately after the sample used to estimate the CAPM regression, calculated for each portfolio.

a) Without writing up complete hypothesis tests, summarise the individual significance of each of the two regressors.

b) Write up a hypothesis test of the overall significance for this regression at the 5% level.

can someone help with the process of how to do these (eg, the R script and the written method?)

harry script dataset Fter average Sample Market Return 0009691395 average Samples average Sample Portfolio Return 0005656364 0.006987160 0011535320 DOOS 000 SOLD 0012073529 0.00 BOSS Os 061385 2009 099133 alpha beta squared residualStandard Error ME1 BM1 -0.0073739777 14219468 04168551 0.07543002 ME1 BM2 -0.0052065456 13162199 04035320 607177088 ME1 BM30.0005750541 13056892 04424347 006573979 ME1 BM40.0017036149 12V19546 A99852 005545637 MET BMS 00010171236 1.1724956 04906952 005357473 ME1 BMS 00043223147 102314 4954511 004676039 ME1 BM7 00030639493 09742907 05246783 004159150 MEL SIM 0 0.0039770135 09345 5557179 003763511 ME1 MO 0.0055008754 09130963 0531213 0036470 ME1 MIO 0.0075201482 10079053 04664966 005232547 ME2 M1 0.0062725662 16127606 05352866 000683117 ME2 BM2 0.0040093452 15133569 6077420 005452966 ME2 8M3 0.001245814515179535 05742771 005120672 ME2 BM400011707172 2345470 0590712 00458004 ME2 BMS 00001703272 06461699 GO0012 ME2 BM OD 11664207 OSS 145 DO ME20M7 00029507 11223206 06191205 00348101 MEZ MB 0.00250 112038 00420735 ME SMOOTW5657 1145107 0.0030700 017667 LT212 00114 MEZ MIODOTTO ASTE 0600015 . ME OM OOG551005 154 0014275931 0012551954 03181588 001450LS 0017590610 0.000 ODOS ODOS 0:00 OOOH45 0010958551 00127 GO BOS COM 0.00 B000 DO OS DOS DIS 00055 Thong 1 to 21 100 1 home Type here to search Detay to file/function Addins dataset wengefutureSample averagefturaSample Market Return 0.000 3.000 05 0.00 COMO . geln Sample MarketReturn average Sample RinkFreeReturn 0.009621385 0001770154 0009691385 0001778154 0.009691365 0.001778154 00091385 0.001770154 0001691355 0.001778154 DO091385 0001778154 0.00967385 0.001778154 0.009691385 0001778154 0.009691385 00017154 0.0096913 0001778154 ODOS 0001770154 0.0009135 0.001778154 0000135 0001772154 ODS 0001778154 0.000 0001778154 COS 0001778154 ODOTT23154 ODO 00011154 0.000 00017112 00017711 154 000 averageFuture Sample Portfolio Return 0.0002137500 00041717917 00092972083 00105645017 COT12760417 0014419870 00170015017 019958 001778 0015 0.000000 ODO 000 OSHO ODOS 60025 ODOS 02009 DO ODOS 50 OS 01511 Det sch harry script dataset Fter average Sample Market Return 0009691395 average Samples average Sample Portfolio Return 0005656364 0.006987160 0011535320 DOOS 000 SOLD 0012073529 0.00 BOSS Os 061385 2009 099133 alpha beta squared residualStandard Error ME1 BM1 -0.0073739777 14219468 04168551 0.07543002 ME1 BM2 -0.0052065456 13162199 04035320 607177088 ME1 BM30.0005750541 13056892 04424347 006573979 ME1 BM40.0017036149 12V19546 A99852 005545637 MET BMS 00010171236 1.1724956 04906952 005357473 ME1 BMS 00043223147 102314 4954511 004676039 ME1 BM7 00030639493 09742907 05246783 004159150 MEL SIM 0 0.0039770135 09345 5557179 003763511 ME1 MO 0.0055008754 09130963 0531213 0036470 ME1 MIO 0.0075201482 10079053 04664966 005232547 ME2 M1 0.0062725662 16127606 05352866 000683117 ME2 BM2 0.0040093452 15133569 6077420 005452966 ME2 8M3 0.001245814515179535 05742771 005120672 ME2 BM400011707172 2345470 0590712 00458004 ME2 BMS 00001703272 06461699 GO0012 ME2 BM OD 11664207 OSS 145 DO ME20M7 00029507 11223206 06191205 00348101 MEZ MB 0.00250 112038 00420735 ME SMOOTW5657 1145107 0.0030700 017667 LT212 00114 MEZ MIODOTTO ASTE 0600015 . ME OM OOG551005 154 0014275931 0012551954 03181588 001450LS 0017590610 0.000 ODOS ODOS 0:00 OOOH45 0010958551 00127 GO BOS COM 0.00 B000 DO OS DOS DIS 00055 Thong 1 to 21 100 1 home Type here to search Detay to file/function Addins dataset wengefutureSample averagefturaSample Market Return 0.000 3.000 05 0.00 COMO . geln Sample MarketReturn average Sample RinkFreeReturn 0.009621385 0001770154 0009691385 0001778154 0.009691365 0.001778154 00091385 0.001770154 0001691355 0.001778154 DO091385 0001778154 0.00967385 0.001778154 0.009691385 0001778154 0.009691385 00017154 0.0096913 0001778154 ODOS 0001770154 0.0009135 0.001778154 0000135 0001772154 ODS 0001778154 0.000 0001778154 COS 0001778154 ODOTT23154 ODO 00011154 0.000 00017112 00017711 154 000 averageFuture Sample Portfolio Return 0.0002137500 00041717917 00092972083 00105645017 COT12760417 0014419870 00170015017 019958 001778 0015 0.000000 ODO 000 OSHO ODOS 60025 ODOS 02009 DO ODOS 50 OS 01511 Det sch