What test do we use if population variances are known?
Answer: z-test: two sample for means
Answer: z-test: two sample for means
Answer: compare the p-value to alpha
Answer: We fail to reject the null hypothesis
Answer: we reject the null hypothesis
Answer: Divides the sampling distribution into a rejection region and a non rejection region
Answer: We need to compare the test statistic to the critical value
Answer: High
Answer: Descriptive analytics
Answer: obj()
Answer: sqrt(65)
Answer: Concatenate (combine)
Answer: z<-4*3
Answer: plot
Answer: rbind
Answer: t.test
Answer: NA
Answer: Clients[,c(1,2)]
Answer: 16
Answer: on the right end of the yellow bar at the bottom of the screen
Answer: an upside down yellow key to the left of the field and "PK" in parentheses to the right of the field
Answer: External Data
Answer: There must be a common field with the same data type between the two tables
Answer: Like "A*"
Answer: change the "required" property to "yes"
Answer: AutoNumber
Answer: an upside down yellow key to the left of the field
Answer: Chi-square test
Answer: factor
Answer: small sample sizes generate a low value of the power of the test
Answer: identifying the population parameter
Answer: The input range must be a rectangular region that contains all data
Answer: the rejection region occurs in the tails of the sampling distribution of the test statistic
Answer: helps determine if the test statistic falls in the rejection region or not
Answer: 1-Beta
Answer: incorrectly fails to reject an actually false null hypothesis
Answer: the null hypothesis is actually true, but the hypothesis test incorrectly rejects it
Answer: null hypothesis
Answer: Its dependent variable is always categorical
Answer: closeness of a record to numerical predictors in the other records
Answer: the distance between the most distant pair of objects, one from each group
Answer: False
Answer: It can contain any non-negative value from the observations
Answer: test data set
Answer: reject or accept credit approval
Answer: Ward's hierarchical clustering
Answer: Divise clustering separates n objects successively into finer groupings
Answer: There is no solution that simultaneously satisfies all the constraints
Answer: Use Automatic Scaling
Answer: there is exactly one solution that will result in the maximum or minimum objective
Answer: Classification
Answer: the skewness is considered to be moderate
Answer: a linear relationship exists for which one variable increases as the other one also increases
Answer: Co-variance
Answer: kurtosis
Answer: A histogram that tails off toward the right
Answer: the observation is 1.0 standard deviations to the right of the mean
Answer: that the Xi value lies to the left of the mean
Answer: NO
Answer: Variance
Answer: A median is not affected by outliers but a mean is.
Answer: MATCH(lookup_value,lookup_array,match_type)
Answer: INDEX(array,row_num,col_num)
Answer: OR(condition 1, condition 2)
Answer: IF(condition, value if true, value if false)
Answer: PivotTable
Answer: PERCENTILE.INC(array,k)
Answer: Surface chart
Answer: Line chart
Answer: $B$4-$B$5*$A8
Answer: ^
Answer: Periodic
Answer: Duality
Answer: a table with a composite primary key
Answer:
DR: A/P
CR: Cash
Answer: A computer process
Answer: Actually creates information
Answer: Participate
Answer: control
Answer: Two related events
Answer: A resource and an event
Answer: An annotation
Answer: Dotted Arrows
Answer: Solid Arrows
Answer: $100 increase in a liability account
Answer:
DR: Cash
CR: A/R
Answer: Perpetual
Answer: Descriptive Analytics
Answer: Prescriptive
Answer: Weight and volume of a sheet of steel
Answer: solve
Answer: messiness
Answer: human capabilities
Answer: in person
Answer: host
Answer: long-term planning
Answer: strategies
Answer: solo variant
Answer: against itself
Answer: popular and complex
Answer: restrictions
Answer: neural networks