The (1 - α)% confidence interval will include the true value of the population parameter with probability 1 - α, i.e., if α =.05, the probability is about.95 that the 95% confidence interval will include the true population parameter. α = the probability a confidence interval will not include the population parameter, 1 - α = the probability the population parameter will be in the interval. 58σ The 99% confidence interval is larger than the 95% confidence interval, and thus is more likely to include the true mean. That is, there is a 95% chance that the following statement will we true: 1.96σ µ σ Similarly, when is normally distributed, the 99% confidence interval for the mean is.58σ µ +. The two boundaries of the interval, 1.96σ and 1.96σ + are called the 95% confidence limits. For example, when is normally distributed, the range of values between ± 1.96σ is called the 95% confidence interval for µ. Because the point estimate is unlikely to be exactly correct, we usually specify a range of values in which the population parameter is likely to be. If, for example, the sampling distribution is approximately normal, then with high probability (about.95) the point estimate falls within standard errors of the parameter. The accuracy of a point estimator depends on the characteristics of the sampling distribution of that estimator. Because of sampling error, we know the point estimate probably is not identical to the population parameter. The particular value chosen as most likely for a population parameter is called the point estimate. graph export "dot and 95 percent ci figure vert.1 Confidence Intervals I. Graph export "dot and 95 percent ci figure vert.png", replace width(2000) , legend(row(1) order(2 "legend 1" 3 "legend 2") pos(6)) /// legend at 6 o’clock position (scatter proportion row if group =2, mcolor(blue)) /// dot for group 2 (scatter proportion row if group =1, mcolor(red)) /// dot for group 1
Stata confidence interval code#
(rcap low95 high95 row, vert) /// code for 95% CI graph export "dot and 95 percent ci figure horiz.tif", replace width(2000) Graph export "dot and 95 percent ci figure horiz.png", replace width(2000) / aspect (next line) is how tall or wide the figure is / also note that there is a space in between different rows by leaving out rows 3, 6, 9, and 12 / note that the labels are 1.5, 4.5, etc so they are between rows 1&2, 4&5, etc. , legend(row(1) order(2 "legend 1" 3 "legend 2") pos(6)) /// legend at 6 o'clock position (scatter row proportion if group =2, mcolor(blue)) /// dot for group 2 (scatter row proportion if group =1, mcolor(red)) /// dot for group 1 (rcap low95 high95 row, horizontal) /// code for 95% CI Import excel "dot and 95 percent ci data.xlsx", firstrow clear That’s it! Code for both versions of the figure follows. If the code won’t work, you probably have Excel open. Doing this will force Stata to set the working directory as the folder containing the.
Stata confidence interval windows#
do file from Windows Explorer and double click it. Close Excel and close Stata then find the.
In the same folder as the Excel file, copy/paste/save the code below as a.
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