power in predicting the response variable. Here the null hypothesis is that the true coefficient is zero. This hypothesis is tested by computing the coefficient's t-statistic, as the ratio of the coefficient estimate to its standard error. If the t-statistic is larger than a predetermined value, the null hypothesis is rejected and the variable is found to have explanatory power, with its coefficient significantly different from zero. Otherwise, the null hypothesis of a zero value of the true coefficient is accepted. In addition, the Chow test is used to test whether two subsamples both have the same underlying true coefficient values. The sum of squared residuals of regressions on each of the subsets and on the combined data set are compared by computing an F-statistic; if this exceeds a critical value, the null hypothesis of no difference between the two subsets is rejected; otherwise, it is accepted. Example with real data The following data set gives average heights and weights for American women aged 30–39 (source: The World Almanac and Book of Facts, 1975). {|class="wikitable" style="text-align:right;" |- ! style="text-align:left;" | Height (m) | 1.47 || 1.50 || 1.52 || 1.55 || 1.57 | rowspan="6" | |- ! style="text-align:left;" | Weight (kg) | 52.21 || 53.12 || 54.48 || 55.84 || 57.20 |- ! style="text-align:left;" | Height (m) | 1.60 || 1.63 || 1.65 || 1.68 || 1.70 |- ! style="text-align:left;" | Weight (kg) | 58.57 || 59.93 || 61.29 || 63.11 || 64.47 |- ! style="text-align:left;" | Height (m) | 1.73 || 1.75 || 1.78 || 1.80 || 1.83 |- ! style="text-align:left;" | Weight (kg) | 66.28 || 68.10 || 69.92 || 72.19 || 74.46 |} When only one dependent variable is being modeled, a scatterplot will suggest the form and strength of the relationship between the dependent variable and regressors. It might also reveal outliers, heteroscedasticity, and other aspects of the data that may complicate the interpretation of a fitted regression model. The scatterplot suggests that the relationship is strong and can be approximated as a quadratic function. OLS can handle non-linear relationships by introducing the regressor 2. The regression model then becomes a multiple linear model: The output from most popular statistical packages will look similar to this: {|style="border:1px solid #aaa; padding:2pt 10pt;" |- | Method || colspan="4" | Least squares |- | Dependent variable || colspan="4" | WEIGHT |- | Observations || colspan="4" | 15 |- |