Applied Healthcare Statistics Final Exam Questions and Answers 2024, Exams of Nursing

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Applied Healthcare Statistics Final Exam Questions and Answers 2024 UPDATE BEST GRADED A+ discrete data - SOLUTION distinct values, can be counted, unconnected points (ex. number of students) continuous data - SOLUTION values are within a range, measured not counted, no gaps between data points (ex. time in a race) less than or greater than - SOLUTION marked with (parenthesis), open circle on number line less than or equal to greater than or equal to - SOLUTION marked with {brackets}, closed/shaded circle on number line prime factorization - SOLUTION writing the number as a product (multiplication) of any prime numbers (ex. 12= 2x2x3) greatest common factor - SOLUTION the largest number that divides all the given numbers evenly (ex. 8 and 10 = 2) least common multiple - SOLUTION the smallest number that can be divided by the given numbers (ex. 16 and 32 = 16) 1 tablespoon = ? teaspoons - SOLUTION 3 teaspoons 1 fluid ounce = ? tablespoons - SOLUTION 2 tablespoons 1 cup = ? ounces - SOLUTION 8 ounces 1 pint = ? cups - SOLUTION 2 cups 1 quart = ? pints - SOLUTION 2 pints 1 gallon = ? quarts - SOLUTION 4 quarts 1 Liter = ? milliliter (mL) - SOLUTION 1000mL 1 kilogram (kg) = ? grams - SOLUTION 1000 grams 1 gram = ? milligrams (mg) - SOLUTION 1000 mg 1 mg = ? mcg - SOLUTION 1000 mcg 1 cc = ? ml - SOLUTION 1 ml 1 fluid ounce = ? ml - SOLUTION 30 ml (or 2 tbsp) 1 Liter = ? quarts - SOLUTION 1.057 quarts 1 tsp = ? mL - SOLUTION 5 mL 1 kg = ? lb - SOLUTION 2.2 lb 1 ounce = ? grams - SOLUTION 28.35 grams 1 tablespoon = ? mL - SOLUTION 15 mL formula to convert Celsius to Fahrenheit - SOLUTION Celsius x 9/5 + 32 formula to convert Fahrenheit to Celsius - SOLUTION Fahrenheit - 32 x 5/9 slope-intercept equation - SOLUTION y = mx + b what does m represent in the slope-intercept equation - SOLUTION slope of the line what does y represent in the slope-intercept equation - SOLUTION y intercept Slope of a line - SOLUTION Rise/Run Rise/Run - SOLUTION (y2 - y1) / (x2 - x1) overall percentages - SOLUTION computed by dividing each frequency by the grand total side by side box plots - SOLUTION C -> Q, a box plot is displayed for each category of the explanatory variable on the same graph scatterplot - SOLUTION Q -> Q data create ordered pairs that are graphed ont he coordinate plane positive correlation - SOLUTION scatterplot, Q -> Q as the explanatory variable increases, the response variable increases negative correlation - SOLUTION scatterplot, Q -> Q as the explanatory variable increases, the response variable decreases no correlation - SOLUTION scatterplot, Q -> Q no trends between variables non-linear relationship - SOLUTION scatterplot, Q -> Q scatterplot reveals a trend that is NOT a straight line second quartile - SOLUTION Q2 the median first quartile - SOLUTION Q1 the median of the data below Q2 third quartile - SOLUTION Q3 the median of the data aboe Q3 standard deviation - SOLUTION the average distance each data point is from the mean Empirical Rule - SOLUTION for normal distributions 68% of data is within 1 standard deviation of the mean 95% of data is within 2 standard deviations of the mean 99.7% of data is within 3 standard deviations of the mean Simpson's Paradox - SOLUTION -occurs when a result that appears in groups of data disappears when the groups are combines -can only occur when the sizes of the groups are inconsistent lurking variables - SOLUTION variable not included in the study but affects the variable that are included in the study regression equation - SOLUTION an equation modeling the relationship between two quantitative variables simple linear equation - SOLUTION AKA regression line or line of best fit -x is the explanatory variable -y is the response variable -equation is y = mx + b -used to predict data (plug in values for x and find corresponding values for y) linear interpolation - SOLUTION predictions between known data points linear extrapolation - SOLUTION predictions larger or small than the known data points p-value - SOLUTION the probability of data occurring by chance significant level - SOLUTION the probability threshold, below which we consider events not happening by chance p-value < significance level - SOLUTION results are significant p-value > significance level - SOLUTION results are not significant experiment - SOLUTION a situation for which a probability is being examined outcome - SOLUTION a possible result of an experiment event - SOLUTION a collection of desired outcomes sample space - SOLUTION -universe -set of all possible outcomes fair - SOLUTION an experiment where all outcomes are equally likely complement of a set - SOLUTION everything NOT in the set disjoint events - SOLUTION contain no common outcomes, cannot happen simultaneously (ex. can't be born on Monday and on Wednesday) dependent events - SOLUTION the occurrence of one event changes the probability of the occurrence of the other event independent events - SOLUTION the occurrence of one even does NOT change the probability of the occurrence of the other event theoretical (classical) probability - SOLUTION number of outcomes in the event divided by the total number of possible outcomes empirical probability - SOLUTION AKA relative frequency -perform the experiment -number of times the event occurs divided by the total number of trials Law of Large Numbers - SOLUTION as the number of trials increase, the empirical probability gets closer to the theoretical probability conditional probability - SOLUTION P(B I A) -the probability of event B happening given that event A has already happened - P(A and B) / P(A) If P(B I A) = P(B) or P (A and B) - P(A) X P(B) - SOLUTION events A and B are independent probability tree - SOLUTION -display all the possible outcomes in a sample space -each path represents a possible outcome -multiply along the path to find probability of that event happening -add the products from each event to find the probability of events that include more than one outcome