6. Introduction to Regression and Correlation
... It is always a good idea to create a Scatter Plot of the data beforehand as a visual check that the assumption of a straight relationship is plausible. Be clear on which variable is independent and which is dependent. The regression of y on x, (y dependent, x independent) is NOT the same as the regr ...
... It is always a good idea to create a Scatter Plot of the data beforehand as a visual check that the assumption of a straight relationship is plausible. Be clear on which variable is independent and which is dependent. The regression of y on x, (y dependent, x independent) is NOT the same as the regr ...
Philip Robbins 10 Apr 2011 IS6010, Case Study #1
... Negative Skew: distribution that is skewed to the left. (trailing tail is on the left,i.e. math test results from PhDs) "skewed to the left" to indicate a "nagative skew" "skewed to the right" to indicate a "positive skew" ...
... Negative Skew: distribution that is skewed to the left. (trailing tail is on the left,i.e. math test results from PhDs) "skewed to the left" to indicate a "nagative skew" "skewed to the right" to indicate a "positive skew" ...
Lab4_Binomial_SampleMean
... As n increases, you should notice a more familiar shape! In the past, people approximated binomial probabilities using normal probabilities when n was large. Now you can see why. Nowadays, computers are powerful enough that the “normal approximation of the binomial” isn’t often needed. ...
... As n increases, you should notice a more familiar shape! In the past, people approximated binomial probabilities using normal probabilities when n was large. Now you can see why. Nowadays, computers are powerful enough that the “normal approximation of the binomial” isn’t often needed. ...
A random sample of 48 days taken at a large hospital shows that an
... fluid ounces and a standard deviation of 0.5 fluid ounces if the machine is calibrated accurately. The quality control manager has collected data on 250 bottles of soda that have come off a production line on a randomly selected day. He wants to find out if the amount of soft drink filled can be rep ...
... fluid ounces and a standard deviation of 0.5 fluid ounces if the machine is calibrated accurately. The quality control manager has collected data on 250 bottles of soda that have come off a production line on a randomly selected day. He wants to find out if the amount of soft drink filled can be rep ...
Chapter 7
... – In statistical inference, the term statistics is used to denote a quantity that is a property of a sample. – Statistics are functions of a random sample. For example, the sample mean, sample variance, or a particular sample quantile. – Statistics are random variables whose observed values can be c ...
... – In statistical inference, the term statistics is used to denote a quantity that is a property of a sample. – Statistics are functions of a random sample. For example, the sample mean, sample variance, or a particular sample quantile. – Statistics are random variables whose observed values can be c ...
Testing Differences between Means continued
... The t ratio is used to make comparisons between two means. The assumption is that we are working with interval level data. We used a random sampling process. The sample characteristic is normally distributed. The t ratio for independent samples assumes that the population variances are equal. ...
... The t ratio is used to make comparisons between two means. The assumption is that we are working with interval level data. We used a random sampling process. The sample characteristic is normally distributed. The t ratio for independent samples assumes that the population variances are equal. ...
CHAPTER 9 TESTS OF HYPOTHESES: LARGE SAMPLES
... 106.5024 We round up to the next whole number. So use n = 107. ...
... 106.5024 We round up to the next whole number. So use n = 107. ...
Why Standard Deviation?
... location and spread of the data between the smallest to the largest value. Percentile tells us the proportion of observations that lie below or above a certain value in the data. Example: Admission test scores for colleges and universities are frequently reported in terms of percentiles. ...
... location and spread of the data between the smallest to the largest value. Percentile tells us the proportion of observations that lie below or above a certain value in the data. Example: Admission test scores for colleges and universities are frequently reported in terms of percentiles. ...
Exploring Data
... Mean verses Median Generally, when you encounter outliers, correct them if wrongly recorded, delete them for good reason, or otherwise give them individual attention. If correctly recorded outliers cannot be discarded, consider using resistant measures. ...
... Mean verses Median Generally, when you encounter outliers, correct them if wrongly recorded, delete them for good reason, or otherwise give them individual attention. If correctly recorded outliers cannot be discarded, consider using resistant measures. ...
Sample size determination
... Sample Confidence “Probability” we can take results as “accurate representation” of universe (i.e. that “sample statistics” are generalisable to the real “population parameters”) ...
... Sample Confidence “Probability” we can take results as “accurate representation” of universe (i.e. that “sample statistics” are generalisable to the real “population parameters”) ...
Bootstrapping (statistics)
In statistics, bootstrapping can refer to any test or metric that relies on random sampling with replacement. Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Generally, it falls in the broader class of resampling methods.Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution function of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples with replacement, of the observed dataset (and of equal size to the observed dataset).It may also be used for constructing hypothesis tests. It is often used as an alternative to statistical inference based on the assumption of a parametric model when that assumption is in doubt, or where parametric inference is impossible or requires complicated formulas for the calculation of standard errors.