A strong and fair health care system
Today, I delivered on my commitment to have a judicial inquiry into allegations of queue jumping in our health care system.
The government response delivered today by Health and Wellness Minister Fred Horne delivers on both my promise and the recommendations made by the Health Quality Council of Alberta.
I said my call for an independent, judicial inquiry is about getting answers and putting a stop to practices that go against my personal and political values. This inquiry will get the answers.
We will take action based on the findings and we will emerge with a stronger health care system that Albertans can trust and have confidence in - a system that provides the care Albertans deserve and supports those who provide that care.
A. FREQUENCY DISTRIBUTIONS
One particularly useful tool for grouping data is the frequency distribution, which is a summary of data presented in the form of class intervals and frequencies.
I. Create a frequency distribution for the Hip Fracture Mortality data (Excel worksheet | column G).
Include columns for Class Interval, Frequency and Relative Frequency (the proportion of the total frequency that is in any given class interval). You can use the class intervals suggested by the software. Ensure that no value of the data can fit into more than one class.
B. GRAPHICAL DEPICTION OF DATA
One of the most effective mechanisms for presenting data in a form meaningful to decision makers is graphical depiction. Through graphs and charts, the decision maker can often get an overall picture of the data and reach some useful conclusions merely by studying the chart or graph.
I. Create a fully labelled histogram (a type of vertical bar chart) for your frequency distribution.
Ensure that the x axis (abscissa) is labelled with the class endpoint and the y axis (ordinate) is labelled with the frequencies.
II. Create a fully labelled pie chart (a circular depiction of data where the area of the whole pie represents 100% of the data and slices represent a percentage breakdown of the sublevels. Pie charts show the relative magnitudes of parts to a whole.) using the relative frequencies that you computed in part A.
A. MEASURES OF CENTRAL TENDENCY, VARIABILITY & SHAPE | UNGROUPED DATA
One type of measure that is used to describe a set of data is the measure of central tendency. Measures of central tendency yield information about the centre, or middle part, of a group of numbers. However, business researchers can use another group of analytic tools, measures of variability, to describe the spread or the dispersion of a set of data. Using measures of variability in conjunction with measures of central tendency makes possible a more complete numerical description of the data. One feature of the standard deviation that distinguishes it from a variance is that the standard deviation is expressed in the same units as the raw data, whereas the variance is expressed in those units squared. Measures of shape are tools that can be used to describe the shape of a distribution of data. The concept of skewness helps us to understand the relationship of the mean, median, and mode. In a unimodal distribution (distribution with a single peak or mode) that is skewed, the mode is the apex (high point) of the curve and the median is the middle value. The mean tends to be located toward the tail of the distribution, because the mean is affected by all values, including the extreme ones.
I. Determine the mean, median and standard deviation of the Postoperative Respiratory Failure (Excel worksheet | column I) and Birth Trauma, Injury to Neonate (Excel worksheet | column J) data.
II. Which has the higher mean? Which data has a distribution with the less skewed?
A. SAMPLING AND SAMPLING DISTRIBUTIONS
Sampling is widely used in business as a means of gathering useful information about a population. Data are gathered from samples and conclusions are drawn about the population as a part of the inferential statistics process.
I. Use the Canadian Hospitals Database to calculate the mean and standard deviation of Failure to Rescue (Excel worksheet | column H). Assume that these figures are true for the population of hospitals in municipalities in Canada. Suppose a random sample of 36 is taken from hospitals in municipalities in Canada. What is the probability that the sample mean of Failure to Rescue is less than 180? What is the probability that the sample mean of Failure to Rescue is between 200 and 240? What is the probability that the sample mean is between 180 and 200?
A. STATISTICAL INFERENCE: ESTIMATION FOR SINGLE POPULATION
On many occasions, estimating the population mean is useful in business research.
I. Use the Canadian Hospitals Database. Construct a 90% confidence interval to estimate Congestive Heart Failure (Excel worksheet | column E) for hospitals. State the point estimate and the error of the estimate. Change the level of confidence to 99%. What happens to the interval? Did the point estimate change?
A. STATISTICAL INFERENCE: HYPOTHESIS TESTING FOR SINGLE POPULATIONS
Business researchers are often called upon to provide insights and information to decision makers to assist them in answering such questions. In searching for answers to questions and in attempting to find explanations for business phenomena, business researchers often develop hypotheses that can be studied and explored. Hypotheses are tentative explanations of a principle operating in nature.
I. Examine the Canadian Hospital Database. Suppose you want to "prove" that the average hospital in the three provinces averages fewer than 210 failures to rescue per year. Use the hospital database as your sample and test this hypothesis. Let alpha be 0.01. On average, do hospitals in these provinces have fewer than 200 acute myocardial infarctions per year? Use the hospital database as your sample and an alpha of 0.10 to test this figure as the alternative hypothesis. Assume that the number of failures to rescue and acute myocardial infarctions in the hospitals are normally distributed in the population.
A. CORRELATION AND SIMPLE REGRESSION ANALYSIS
Correlation is a measure of the degree of relatedness of variables. Regression analysis is a mathematical model or function that can be used to predict or determine one variable by another variable or other variables. The most elementary regression model is called simple regression or bivariate regression and involves two variables in which one variable is predicted by another variable. In simple regression, the variable to be predicted is called the dependent variable and is designated as y. The predictor is called the independent variable, or explanatory variable, and is designated as x.
I. Using the Canadian Hospital Database, develop a regression model to predict the number of Congestive Heart Failure by the Population of the municipality. Now develop a regression model to predict number of Acute Stroke Mortality from the Population of the municipality. Examine the regression output. Which type of mortality is more strongly associated with the Population? Explain why, using techniques presented in chapter 12. Use the second regression model to predict the number of Acute Stroke Mortality in municipalities that have a Population of 24,000 or more. Construct a 95% confidence interval around this prediction for the average value of y.