MS Modules 18 and 19 – units of measurement practice problems


MS Modules 18 and 19 – units of measurement practice problems

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MS Modules 18 and 19 – units of measurement practice problems

(The attached PDF file has better formatting.)

Variables and parameters have two parts: an amount and a unit of measurement. Regression parameters and the least squares estimates depend on the units of the explanatory and response variables.

Illustration: Suppose Yj = β0 + β1 X1j + β2 X2j + εj

The items in the sum – Yj, β0, β1 × X1j, β2 × X2j, and εj – are in the same units, so

●    β0 (and its estimate 0) has the same units as Y
●    ε (the error term) and the regression residuals have the same units as Y
●    β1 × X1 has the same units as Y, so β1 (and its estimate 0) has the same units as Y/X1

A change in the units of a parameter causes an offsetting change in the magnitude of the parameter.

Illustration: Suppose an explanatory variable is X = 2,500 meters and β1 = 0.03.

If the units of the explanatory variable change to kilometers (1 kilometer = 1,000 meters) and the units of the response variable do not change, then

●    the amount of X changes to 2.5 (divide by 1,000)
●    β1 changes to 30 (multiply by 1,000) so that β × X does not change

1, the ordinary least squares estimator of β1, changes the same way as β1. If 1 = 0.03 when X is in meters, it is 30 when X is in kilometers.

The square of a random variable is in units-squared of the random variable.

Illustration: If Y is measured in meters, SST, SSE, and SSR, are in meters-squared. If the units of Y change from meters to kilometers, the values of Y are divided by 1,000, and the value of SST, SSE, and SSR are divided by 1,000² = 1,000,000.

Ratios are in units of the numerator divided by units of the denominator.

Illustration: The R2 is the regression sum of squares (SSR) divided by the total sum of squares (SST). SSR and SST are measured in the same units, so the R2 is unit-less.

The number of observations (N) and the number of parameters (k) in the regression analysis are unit-less. If the explanatory variable changes from meters to kilometers, the number of observations and the number of parameters do not change. The adjusted R2 = 1 – (1 – R2) × (N – 1) / (N – k – 1) , so the adjusted R2 is also unit-less.

Standard deviations and standard errors are in the same units as the random variable they apply to.

Illustration: The standard error of 1 is in the same units as 1; the standard error of 0 is in the same units as 0; the standard error of the error term ε is in the same units as ε (which is the same units as the response variable Y and the residual e).

Many statistical measures are unit-less: they do not depend on the units of measurement of the explanatory or response variables.

Standardized values, such as standardized residuals, are the values divided by their standard errors, so they are unit-less.

Illustration: If the response variable changes from dollars to euros or from degrees Fahrenheit to degrees Centigrade, the standardized residuals do not change.

The t value for a least squares estimate is a standardized value, so it is unit-less. The t value measures the significance of the regression equation: changing the units of measurement does not change the significance.

Illustration: The t value is the estimated parameter divided by its standard error. A random variable and its standard error have the same units, so the t value is unit-less.

p values are measures of significance, so they are unit-less. They measure the percentage of scenarios with outcomes at least as extreme as the observed data (assuming random fluctuations and a null hypothesis). The p values are independent of the units of measurement of explanatory and response variables.

Know how ordinary least squares estimators, their standard errors, t-values, and p-values depend on the units of measurement and displacement from the origin. The principles are

●    Multiplying the explanatory variable by k multiplies its βj by 1/k.
●    Multiplying the response variable by k multiplies all the β’s by k.
●    Displacements of explanatory variables and the response variable from the origin changes only β0, not the slope coefficients.

Intuition: Knowing the units of the regression coefficients helps determine how a change in the units affects the coefficients.

●    β0 is in units of the response variable, not in units of the explanatory variables.
●    β1 is in units of response variable / explanatory variable.

Illustration: Suppose claim frequency = β0 + β1 × kilometers driven.

●    β0 is in units of claim frequency.
●    β1 is in units of claim frequency / kilometers driven

If we write the regression equation as claim frequency = β0 + (β1/1,000) × meters driven.

●     β0 is in units of claim frequency.
●     β1 is in units of claim frequency / kilometers driven ➾ β1 / 1,000 is in units of claim frequency / meters.

Intuition: β1 depends on the deviations of the values from their mean. A constant displacement of all the values doesn’t affect the deviations. But a constant displacement of k raises the response variable Y by k × β1. β0 has the same displacement as the response variable, so it also rises by k × β1.

Standardized coefficients and t-values are unit-less.

Standardized coefficients are β × σx / σy.

●     β is in units of Y / X.
●     σx is in units of X.
●     σy. Is in units of Y.

➾ The standardized coefficient is unit-less.

Measures of significance are not affected by units of measurement.

●    The t-value is the ordinary least squares estimator divided by its standard deviation.
●    The estimator and its standard deviation have the same units, so the t-value is unit-less.

The correlation between two random variables is unrelated to units of measurement, so the R2 statistic is also unit-less.

Exercise 18.1: Sample size and expected values

A statistician regresses the response variable (Yj) on the explanatory variable (Xj), Yj = β0 + β1Xj + εj, with N = 5 observations, gives 1 (the least squares estimate for β1) = 1. The null hypothesis is β1 = 0. The X values here are randomly sampled from a population (e.g., X and Y are a bivariate normal distribution).

As N increases, what happens to the expected values of the following?

A.    R2
B.    The adjusted R2
C.    σX, the standard deviation of X, or √(Sxx/(n-1))
D.    σ(1), the standard error of 1
E.    The t value for the null hypothesis H0: β1 = 0
F.    The p value for the significance of the t test above
G.    The width of the 95% confidence interval for β1

Part A: As N increases, the expected value of R2 decreases. The change in R2 is noticeable for small values of N. For larger values of N, the change in the expected value of R2 is small.

Part B: The adjusted R2 is adjusted for degrees of freedom. A change in the number of observations changes the degrees of freedom, so the formula for the adjusted R2 has an offsetting change in (N – 1) / (N – k) and the expected value of the adjusted R2 does not change.

Intuition: With N = 2, the correlation of the explanatory variable and the response variable is +1 or –1, and the R2 of the regression line is 100%, even if the two variables are independent. With N = 3 and a independent variables, the correlation is ±1 just by random fluctuations in one third of scenarios, and the expected value of R2 is 50%.

Question: How do we derive the expected value of R2?

Answer: If the explanatory variable and the response variable are independent, the expected value of the adjusted R2 is zero. Using the formula

    Adjusted R2 = 1 – (1 – R2) × (N – 1) / (N – k – 1)

we derive

    R2 = 1 – (1 – adjusted R2) × (N – k – 1) / (N – 1)

For N = 3 and k = 1, we have

    R2 = 1 – (1 – 0) × (3 – 1 – 1) / (3 – 1) = ½ = 50%.

Part C: The population variance and standard deviation of X are not affected by the number of observations in the regression analysis. The sample variance and standard deviation of X may be higher or lower than the population variance and standard deviation, but the difference is random and has an expected value of zero.

Question: Why does the practice problem say that the X values are randomly sampled from a population? Isn’t this always true?

Answer: The dependent variable Y is a random variable with mean μj (= the fitted value at X = xj) and variance σ2 (equal at all points). The independent variable X is not necessarily a random variable. It may be chosen for the experiment, such as the first N positive integers. (The variance of the first N integers depends on N.)

Question: Does Sxx, the sum of squared deviations of the X values, depend on the number of observations if the X values are a random sample from a population?

Answer: The variance is the sum of squared deviations divided by N-1. The variance is independent of N, so the expected value of the sum of squared deviations is proportional to N-1.

Part D: The variance of 1 is the estimated value of σ2 divided by the sum of squared deviations of X. Since the estimated value of σ2 is independent of N and the sum of squared deviations of X is proportional to N-1, the variance of 1 is proportional to 1/(N-1), and the standard error of 1 is proportional to 1/√(N-1). As N increases, the expected value of the standard error of 1 decreases.

Part E: The t value for 1 is the value of 1 divided by its standard error.

●    The expected value of 1 does not change with the number of observations.
●    The expected value of the standard error of 1 is proportional to 1/√(N-1).
●    ➾ The expected value of the t value for 1 is proportional to √(N-1).

Part F: The p value for the significance of 1 decreases as the t value for 1 increases. This p value depends on the cumulative distribution function of the t distribution, which does not have a simple algebraic form.

Question: Do the degrees of freedom also affect the change in the p value?

Answer: More observations give a more compact (less heavy tailed) t distribution and a lower p value.

●    As the t value increases, the p value decreases, even if N does not change.
●    As the N increases, the p value decreases, even if the t value does not change.

Part G: The width of the 95% confidence interval for 1 is 2 × t5%/2, n-2 × σ(1). More observations increase the degrees of freedom and reduce the standard deviation of 1 and reduce the width of the confidence interval.

Exercise 18.2: Miles Driven and Annual Claim Costs

A U.S. actuary uses least squares regression with N pairs of observations (Xi, Yi) to estimate average annual claims cost in dollars per average miles driven per day, giving Y= 50 + 40X + ε. For instance, a policyholder who drives an average of 25 miles a day has average claim costs of 50 + 40 × 25 = 1,050 dollars a year. The null hypothesis is H0: β1 = 0.

A European actuary uses the relation to estimate annual claims costs in Euros based on kilometers driven a day. For this problem, assume €1.00 = $1.25 and 1 kilometer = ⅝ mile (five eighths of a mile).

A.    What is the percentage change in the intercept parameter β0?
B.    What is the percentage change in the slope coefficient β1?
C.    What is the percentage change in the variance σ2?
D.    What is the percentage change in the standard error of 1?
E.    What is the percentage change in the t value to test the null hypothesis?
F.    What is the percentage change in the F value to test the null hypothesis?
G.    What is the percentage change in the width of the 95% confidence interval for β?
H.    What is the percentage change in the p value for the test of the null hypothesis?
I.    What is the percentage change in the total sum of squares?
J.    What is the percentage change in the regression sum of squares?
K.    What is the percentage change in the error sum of squares?
L.    What is the percentage change in the R2?

Part A: The intercept parameter β0 is the value of the response variable (dependent variable) when the explanatory variable is zero. It does not depend on the value of the explanatory variable, so the units of measurement for the explanatory variable are not relevant.

The intercept parameter β0 has the same units as the response variable. If these units increase by a factor of UM, β0 increases by a factor of UM.

Illustration: An intercept of β0 dollars = β0 × 1.00 / 1.25 Euros (assuming €1.00 = $1.25) = β0 × 0.80 Euros.

If β0 = $10 for the regression model in dollars, then β0 = €8 for the regression model in euros.

Part B: If the explanatory variable changes by 1, the response variable changes by β1. The slope coefficient β1 has units of dollars / miles. If the explanatory variable changes by 1 mile, the response variable changes by β1 dollars.

If the explanatory variable changes by 1 kilometer, it changes by ⅝ × 1 mile, so the response variable changes by ⅝ × β1 dollars = ⅝ × β1 × 0.80 euros. The slope coefficient β1 for the regression equation in kilometers and euros changes by a factor of ⅝ × 0.80 = 0.50.

Illustration: Suppose β1 = $0.20 per mile for the regression model in dollars. An insured who drives 400 miles per annum has an annual cost that is $0.20 per mile × 100 miles per annum = $20 more per annum than an insured who drives 0 miles per annum.

●    With the $1.25 = €1.00 exchange rate, $20 = €20/1.25 = €16.
●    100 miles = (1 / ⅝) × 100 = 160 kilometers.

160 kilometers per annum increases annual costs by €16, so β1 = €16 / 160 kilometers per annum = 0.10.

Part C: The units of ε (the error term) are the same as the units of the response variable and of β0, so the units of σ are these units as well and the units of σ2 are the square of these units. If the change in the units causes β0 to be multiplied by 0.80, each error term is multiplied by 0.80, σ is multiplied by 0.80, and σ2 is multiplied by 0.80 × 0.80 = 0.64.

Illustration: If σ2 = 25 dollars2 for the regression model in dollars, then σ2 = 25 dollars2 × (0.8 dollars/euros)2 = 16 euros2 for the regression model in euros.

Parts E, F, L: Tests of significance do not change. If the regression equation in miles and dollars is significant, the regression equation in kilometers and euros is significant. The t value, p value, and F value do not change.

The t value is 0 divided by its standard error. The standard error (standard deviation) of a random variable is in the same units of measurement as the random variable itself, so the t value does not change when the units of measurement change. The F value is the square of the t value, so it also does not change. The p value is the significance level of the t value, so it also does not change.

Part G: The width of the 95% confidence interval for β1 is 2 × σ(β1) × t5%, df, where t5%, df is the t value for a two-sided 95% confidence interval for a t distribution with degrees of freedom = df. The t value does not depend on the units of measurement, so the width of the 95% confidence interval is proportional to σ(β1) . In this example, β1 is multiplied by 0.50, so σ(β1) is multiplied by 0.50, and the width of the 95% confidence interval is multiplied by 0.50.

Part I: The total sum of squares SST depends on the values of the response variable. All these values are squared, so if the response variable changes by a factor of UM, the SST changes by a factor of UM2.

Illustration: If the total sum of squares for the regression model in dollars is 1000 dollars2, it is 1000 dollars2 × (0.8 euros/dollar)2 = 640 euros2 for the regression model in euros.

Part J: The regression sum of squares SSR (= SST – SSE) is the portion of the total sum of squares explained by the explanatory variable. The proportion does not depend on the units of measurement: the units of SST, SSE, and SSR are the same. If the response variable changes by a factor of UM, SSR changes by a factor of UM2.

Part K: SSE = SST – SSR, so it changes by the same ratio as SST and SSR.

SSE = σ2 × the degrees of freedom. The degrees of freedom does not change when units of measurement change. In this example, σ2 is multiplied by 0.64, so SSE is multiplied by 0.64.

Part L: SST, SSR, and SSE all change by the same percentage factor. The R2 is SSR / SST = 1 – SSE/SST, so it does not change.

Exercise 18.3: Fahrenheit vs Centigrade

An American meteorologist regresses annual global warming (the response variable) in degrees Fahrenheit on coal burned (the independent variable) in billions of pounds:

    global warming = β0 + β1 × energy use + ε

The response variable (global warming) is the change in the daily temperature over the year.

The least squares estimate for β0 is 0.1 and the least squares estimate for β1 is 0.01.

The meteorologist modifies the analysis to use degrees Centigrade and billions of kilograms for a presentation in Europe. The formula to convert degrees Fahrenheit to degrees Centigrade is

    degrees Centigrade = (degrees Fahrenheit – 32̊) × 5/9

For the weights, use 1 kilogram = 2.2 pounds (an approximate formula).

A.    If no coal is burned, what is the modeled global warming in degrees Fahrenheit?
B.    What is β0 for the regression model in degrees Centigrade?
C.    For each billion kilograms of coal burned, what is the extra global warming in degrees Centigrade?

Part A: If no coal is burned, the expected global warming = β0 + β1 × 0 = 0.1 degrees Fahrenheit.

Part B: Warming is the change in temperature:

    degrees Centigrade = (degrees Fahrenheit – 32̊) × 5/9 ➾
    change in degrees Centigrade = (change in degrees Fahrenheit) × 5/9

β0 for the regression model in degrees Centigrade = 0.1 × 5/9 = 0.55556.

Part C: For each billion pounds of coal burned, the extra global warming in degrees Fahrenheit is 0.01, so

β1 = 0.01 degrees Fahrenheit / pounds =

β1 = 0.01 × 5/9 degrees Centigrade / ( 1 / 2.2 kilograms) = 0.01 × (5/9) × (1/2.2) = 0.00253


Exercise 18.4: Miles Driven and Annual Claim Costs

We use least squares regression with N pairs of observations (Xi, Yi) to estimate average annual claims cost in dollars per average miles driven per day, giving Y= 50 + 40X + ε. For instance, a policyholder who drives an average of 25 miles a day has average claim costs of 50 + 40 × 25 = 1,050 dollars a year.

If we change the parameters to annual claims costs in Euros and kilometers driven a day:

A.    What is the revised value of β1?
B.    What is the revised value of β0?

Assume €1.00 = $1.25 and 1 kilometer = ⅝ mile (five eighths of a mile).

Part A: The estimate of β1 is the covariance of X and Y divided by the variance of X.

●    Using euros multiplies each Y value by 1.00 / 1.25 = 0.80.
●    Using kilometer multiplies each X value by 8/5 = 1.60.

Illustration: $10.00 = 10 × 0.80 = €8.00, and 10 miles = 10 × 1.60 = 16 kilometers.

Multiplying the Y values by 0.80 and the X values by 1.60

●    Multiplies the covariance by 0.80 × 1.60 = 1.280
●    Multiplies the variance of X by 1.602 = 2.560

This multiplies β1 by 1.280 / 2.560 = 0.500.

Part B: β0 is not affected by the units of X, since the product β1 × X is not affected by the units of X. But β0 varies directly with the units of Y: if Y is multiplied by 0.80, β0 is multiplied by 0.80.

Question: Is the product β1 × X unit-less?

Answer: No; the product is in the units of Y.

We can check our result numerically:

●    Before the change, if X = 0 miles, Y = $50. Now X = 0 gives Y = €40, so β0 is 40.
●    Before the change, if X = 5 miles, Y = $250. Now X = 8 kilometers gives Y = $250 × 0.8 = €200. Since β0= 40, β1 is (200 – 40) / 8 = 20.



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