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Free CFA Quantitative Methods Practice Questions & Answers

1,200 exam-style Quantitative Methods questions. Pick your answer, hit Check answer, and see the worked solution — free to start, no signup.

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Question 181

A series that fluctuates around a constant mean with constant variance and no trend is:

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Question 182

If first‑differencing a series makes it stationary, the original series is:

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Question 183

A non‑stationary series with a unit root is often modeled by:

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Question 184

A stationary series is required for:

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Question 185

A linear trend model assumes the variable:

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Question 186

If a variable grows at a constant percentage rate, the natural log of the variable over time will appear:

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Question 187

A high R² in a trend regression mainly indicates:

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Question 188

A log‑linear trend model predicts future values by:

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Question 189

A linear trend model is likely inappropriate if the data:

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Question 190

A trend model with strongly serially correlated residuals suggests:

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Question 191

A log‑linear model is preferred over a linear model when:

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Question 192

A trend model with a Durbin–Watson statistic near zero likely has:

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Question 193

A limitation of simple trend models is that they often:

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Question 194

A Durbin–Watson statistic close to 2 suggests:

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Question 195

A Durbin–Watson statistic far below 2 suggests:

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Question 196

Durbin–Watson is not valid when the regression includes:

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Question 197

Serial correlation in regression errors mainly affects:

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Question 198

A model with serially correlated errors violates the assumption of:

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Question 199

Residual autocorrelation can be tested by:

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Question 200

A regression model shows a Durbin–Watson statistic of 0.25. This most likely indicates:

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Question 201

Residual autocorrelation is considered significant when:

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Question 202

If several residual autocorrelations are statistically significant, the model is likely:

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Question 203

An AR(1) model predicts the next value using:

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Question 204

An AR(2) model includes:

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Question 205

For an AR(1) model to be stationary, the lag coefficient must be:

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Question 206

A lag coefficient of 0.95 in an AR(1) model indicates:

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Question 207

A lag coefficient greater than 1 in absolute value implies the series is:

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Question 208

The long‑run mean of a stationary AR(1) model is:

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Question 209

If the current value of an AR(1) series is below its long‑run mean, the next forecast will:

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Question 210

Including a lag of 4 in a quarterly model is typically done to capture:

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Free CFA Quantitative Methods Practice Questions & Answers

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