STAT7055 Introductory Statistics for Business and Finance
The Complete Exam Bible · S2 2026STAT7055 Overview
- The Australian National University
- Second Semester, 2026
- a postgraduate course
- 6 units
- a statistics course for business
STAT7055 Introductory Statistics for Business and Finance develops descriptive statistics, probability, estimation, hypothesis testing, analysis of variance and regression for financial and investment data. It is taught within ANU Research School of Finance, Actuarial Studies and Statistics. It is a postgraduate course.
- Assessment split 30% mid-semester exam, 20% R-based assignment and 50% final exam; the Canvas progress quiz carries 0%.
- Exam control Both published examinations are in person and exclude dictionaries; the class-summary dates are earliest possible dates.
- Inference habit Name the population, sampling structure and assumption before interpreting a probability, interval, test or regression coefficient.
- Source wrinkle The official weekly schedule ends in the published 11-12-10 topic-number order, which this guide keeps visible.
How STAT7055 is assessed
| Component | Weight | Format |
|---|---|---|
| Canvas Progress Quiz | 0% | Online feedback quiz in Week 6; listed date is the earliest possible date |
| Mid-Semester Examination | 30% | In person; likely 60 minutes; Topics 1-6; dictionaries not permitted; not redeemable |
| Individual Assignment | 20% | R and RStudio analyses reported individually through Canvas; not redeemable |
| Final Examination | 50% | In person; likely 120 minutes; full semester; dictionaries not permitted |
ANU publishes a 0% progress quiz, 30% mid-semester exam, 20% individual R assignment and 50% final exam. The two exam dates are labelled as earliest possible dates. No component-level pass hurdle appears in the captured class summary.
Current STAT7055 dates
| Date | Item | Control |
|---|---|---|
| 21 September 2026 | Mid-semester exam earliest possible date | ANU says the actual sitting details will be supplied in Canvas. |
| 16 October 2026 | Assignment due | Individual R and RStudio report submitted through Canvas. |
| 5 November 2026 | Final exam earliest possible date | Confirm the official examination timetable. |
Dates are as published in the official ANU class summary. Confirm exact deadlines and submission settings in the live LMS.
What STAT7055 covers
Start with Describing Financial and Investment Data; use Hypothesis Tests and Statistical Decisions as the turning point; finish by bringing the course together in Chi-Squared Tests for Categorical Data.
Describing Financial and Investment Data
centre and variability · distribution shape · standardised score · audit a financial data set before selecting any probability model or inferential procedureDefine centre and variability
The captured teaching materials give this chapter a concrete anchor: The lecture treats standard deviation as investment-return risk and introduces covariance because assets must be assessed jointly when a portfolio is formed.
- centre and variability
- distribution shape
- standardised score
- Applying centre and variability
- Limits of distribution shape and standardised score
Probability Rules and Conditional Reasoning
sample space · conditional probability · independence · translate a business event into sets and use conditional information without confusing disjointness with independenceDefine sample space
The captured teaching materials give this chapter a concrete anchor: The teaching set uses a fund manager's program rank and subsequent market outperformance to distinguish a joint event from a probability conditional on the ranked group.
- sample space
- conditional probability
- independence
- Applying sample space
- Limits of conditional probability and independence
Discrete Random Variables and Expected Value
discrete random variable · probability mass function · expected value · build a discrete distribution and connect expected value and variability to a repeated financial decisionDefine discrete random variable
The captured teaching materials give this chapter a concrete anchor: The lecture forms a portfolio with 25% in one stock and 75% in another, showing that expected return is a weighted centre while portfolio variance also changes with correlation.
- discrete random variable
- probability mass function
- expected value
- Applying discrete random variable
- Limits of probability mass function and expected value
Continuous Distributions and Probability Areas
continuous random variable · probability density · normal distribution · convert a continuous model into interval probabilities and interpret the role of location and scaleDefine continuous random variable
The captured teaching materials give this chapter a concrete anchor: A normal-salary example converts a top-tail proportion into a cutoff, while a soft-drink filling example frames the other direction: choosing a setting to control overflow risk.
- continuous random variable
- probability density
- normal distribution
- Applying continuous random variable
- Limits of probability density and normal distribution
Sampling Distributions and Standard Error
random sample · sampling distribution · standard error · separate variation among observations from variation among repeated sample statisticsDefine random sample
The captured teaching materials give this chapter a concrete anchor: The lecture enumerates all sample means from two rolls of a four-sided die, making the sampling distribution an exact repeated-sample object rather than a verbal abstraction.
- random sample
- sampling distribution
- standard error
- Applying random sample
- Limits of sampling distribution and standard error
Estimation and Confidence Intervals
point estimate · confidence interval · margin of error · construct and interpret an interval while showing the estimate, uncertainty and model conditions separatelyDefine point estimate
The captured teaching materials give this chapter a concrete anchor: The estimation materials separate the repeated-sampling meaning of confidence from the factors controlling interval width, then use a bus-trip setting to work backward to minimum sample size.
- point estimate
- confidence interval
- margin of error
- Applying point estimate
- Limits of confidence interval and margin of error
Hypothesis Tests and Statistical Decisions
null and alternative hypotheses · test statistic · p-value and decision · state hypotheses, evaluate compatibility with the null model and separate the statistical decision from the business conclusionDefine null and alternative hypotheses
The captured teaching materials give this chapter a concrete anchor: A house-price example tests a benchmark near $400,000 and evaluates Type II error and power when the true mean is lower, so non-rejection is tied to a specified alternative rather than treated as proof.
- null and alternative hypotheses
- test statistic
- p-value and decision
- Applying null and alternative hypotheses
- Limits of test statistic and p-value and decision
Comparing Two Populations
independent groups · paired observations · standard error of a difference · choose an independent or paired comparison from the data-generating structure and interpret the estimated differenceDefine independent groups
The captured teaching materials give this chapter a concrete anchor: The lecture compares weights from current and new containers using two samples and checks the variance relationship before choosing the mean-comparison procedure.
- independent groups
- paired observations
- standard error of a difference
- Applying independent groups
- Limits of paired observations and standard error of a difference
Analysis of Variance Across Groups
between-group variation · within-group variation · F statistic · use an F comparison to test group-mean evidence and plan follow-up comparisons without inflating errorDefine between-group variation
The captured teaching materials give this chapter a concrete anchor: The ANOVA topic uses grouped outcomes such as tutor-related grades and graduate salaries to connect an omnibus F comparison with the need for controlled follow-up comparisons.
- between-group variation
- within-group variation
- F statistic
- Applying between-group variation
- Limits of within-group variation and F statistic
Simple Linear Regression
response and explanatory variables · least-squares slope · residual · fit and interpret a one-predictor regression while checking units, residual patterns and extrapolationDefine response and explanatory variables
The captured teaching materials give this chapter a concrete anchor: The simple-regression lecture links slope interpretation to fitted values and residual diagnostics, keeping prediction inside the observed predictor range.
- response and explanatory variables
- least-squares slope
- residual
- Applying response and explanatory variables
- Limits of least-squares slope and residual
Multiple Linear Regression
partial regression coefficient · confounding · model fit · interpret a coefficient conditionally and test whether adding predictors changes precision, bias risk and substantive meaningDefine partial regression coefficient
The captured teaching materials give this chapter a concrete anchor: The final regression topic uses multi-predictor settings including attitudes and house prices to distinguish a partial fitted association from an uncontrolled causal claim.
- partial regression coefficient
- confounding
- model fit
- Applying partial regression coefficient
- Limits of confounding and model fit
Chi-Squared Tests for Categorical Data
contingency table · expected count · chi-squared statistic · compare observed and expected counts to assess categorical association and locate influential cellsDefine contingency table
The captured teaching materials give this chapter a concrete anchor: The categorical-data topic uses college-composition and company-survey tables to derive expected counts from margins and locate cells driving an association result.
- contingency table
- expected count
- chi-squared statistic
- Applying contingency table
- Limits of expected count and chi-squared statistic
It carries 6 units. It is positioned as a statistics course for business, finance and related postgraduate study.
The 2026 class sequence moves from describing a data set to reasoning about random variables and sampling, then to inference and regression.
A zero-weight Canvas quiz supplies progress feedback before the two examinations and the R-based assignment.
Assessment in STAT7055 is distributed as follows: 0% Canvas progress quiz, 30% in-person mid-semester examination, 20% individual R and RStudio assignment, and 50% in-person final examination
The operational assessment conditions matter here.
The mid-semester examination is likely 60 minutes, in person and covers Topics 1-6; the final is likely 120 minutes, in person and covers the full semester.
Dictionaries are not permitted in either examination, and the listed dates are earliest possible dates rather than guaranteed sittings.
What makes STAT7055 demanding is concrete: Choosing an inferential method from the sampling structure, carrying its assumptions through the calculation, and translating the numerical output into a conclusion that remains about the stated population and business or finance question.
No component-level pass hurdle is published in the captured class summary.
The mid-semester exam and assignment are described as not redeemable; students must confirm any later operational rule in Canvas.
For enrolment planning, Confirm eligibility in the current ANU Programs and Courses entry; the captured class summary does not publish a prerequisite statement.
Start with Describing Financial and Investment Data; use Hypothesis Tests and Statistical Decisions as the turning point; finish by bringing the course together in Chi-Squared Tests for Categorical Data.
Coverage note: the official schedule labels the final three teaching entries Topic 11, Topic 12 and Topic 10 in that order; this guide preserves the published teaching order and does not silently renumber it.
Worked example · free
From a finance sample to a bounded confidence statement
- 1Compute the standard error as 3.0% divided by the square root of 36, giving 0.5%.
- 1Compute the approximate margin 1.96 times 0.5%, giving 0.98%.
- 1Form the interval 1.4% plus or minus 0.98%, or about 0.42% to 2.38%.
- 1Tie the interpretation to the sampling procedure and the target mean rather than to individual months.
- 1State that dependence, selection or a poor model can invalidate the approximation.
Key terms
- Sampling distribution
- The probability distribution of a statistic across repeated samples drawn under the same design.
- Analysis of variance
- A method that partitions variability to compare group means under a stated statistical model.
- Multiple linear regression
- A linear model relating one response variable to two or more explanatory variables simultaneously.
- Descriptive statistics
- Numerical and graphical methods used to summarise the location, spread and shape of observed data.
- Random variable
- A rule that assigns a numerical value to each outcome of a random process.
- Probability distribution
- A specification of the possible values of a random variable and their associated probabilities.
- Confidence interval
- A range calculated by a procedure designed to capture an unknown parameter at a stated long-run rate.
- Hypothesis test
- A decision procedure that evaluates sample evidence against a specified null model and significance rule.
- Chi-squared test
- A test comparing observed categorical counts with counts expected under a specified null relationship.
- Simple linear regression
- A linear model relating one response variable to one explanatory variable through an intercept and slope.
- Standard error
- The standard deviation of a statistic's sampling distribution, estimating its repeated-sample variability.
STAT7055 FAQ
Which current STAT7055 dates are captured?
Mid-semester exam earliest possible date: 21 September 2026; Assignment due: 16 October 2026; Final exam earliest possible date: 5 November 2026. Confirm any change and the exact submission setting in the live LMS.
Where do students usually lose marks in STAT7055?
Choosing an inferential method from the sampling structure, carrying its assumptions through the calculation, and translating the numerical output into a conclusion that remains about the stated population and business or finance question.
What is the STAT7055 exam or final-task format?
The mid-semester examination is likely 60 minutes, in person and covers Topics 1-6; the final is likely 120 minutes, in person and covers the full semester. Dictionaries are not permitted in either examination, and the listed dates are earliest possible dates rather than guaranteed sittings.
Does STAT7055 have a hurdle or component-level pass rule?
No component-level pass hurdle is published in the captured class summary. The mid-semester exam and assignment are described as not redeemable; students must confirm any later operational rule in Canvas.
What prerequisites or restrictions apply to STAT7055?
Confirm eligibility in the current ANU Programs and Courses entry; the captured class summary does not publish a prerequisite statement.
Is this STAT7055 resource an official university guide?
No. It is an independent STAT7055 study resource; current institutional instructions remain authoritative for assessment operation.
Questions students ask in each STAT7055 chapter
- What is the main task in Describing Financial and Investment Data?
- Audit a financial data set before selecting any probability model or inferential procedure. Chapter 01 · Describing Financial and Investment Data
- What is the main task in Probability Rules and Conditional Reasoning?
- Translate a business event into sets and use conditional information without confusing disjointness with independence. Chapter 02 · Probability Rules and Conditional Reasoning
- What is the main task in Discrete Random Variables and Expected Value?
- Build a discrete distribution and connect expected value and variability to a repeated financial decision. Chapter 03 · Discrete Random Variables and Expected Value
- What is the main task in Continuous Distributions and Probability Areas?
- Convert a continuous model into interval probabilities and interpret the role of location and scale. Chapter 04 · Continuous Distributions and Probability Areas
- What is the main task in Sampling Distributions and Standard Error?
- Separate variation among observations from variation among repeated sample statistics. Chapter 05 · Sampling Distributions and Standard Error
- What is the main task in Estimation and Confidence Intervals?
- Construct and interpret an interval while showing the estimate, uncertainty and model conditions separately. Chapter 06 · Estimation and Confidence Intervals
- What is the main task in Hypothesis Tests and Statistical Decisions?
- State hypotheses, evaluate compatibility with the null model and separate the statistical decision from the business conclusion. Chapter 07 · Hypothesis Tests and Statistical Decisions
- What is the main task in Comparing Two Populations?
- Choose an independent or paired comparison from the data-generating structure and interpret the estimated difference. Chapter 08 · Comparing Two Populations
- What is the main task in Analysis of Variance Across Groups?
- Use an f comparison to test group-mean evidence and plan follow-up comparisons without inflating error. Chapter 09 · Analysis of Variance Across Groups
- What is the main task in Simple Linear Regression?
- Fit and interpret a one-predictor regression while checking units, residual patterns and extrapolation. Chapter 10 · Simple Linear Regression
- What is the main task in Multiple Linear Regression?
- Interpret a coefficient conditionally and test whether adding predictors changes precision, bias risk and substantive meaning. Chapter 11 · Multiple Linear Regression
- What is the main task in Chi-Squared Tests for Categorical Data?
- Compare observed and expected counts to assess categorical association and locate influential cells. Chapter 12 · Chi-Squared Tests for Categorical Data
How to study for the exam
Retrieve the course map, practise the recurring method—identify the population and variables, select a probability or inferential model from the sampling structure, calculate transparently and translate the result into a qualified finance or business conclusion—on changed scenarios, and verify every operational assessment detail in the live institutional system.
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