26134

26134 · Responsible Evidence-Based Decisions26134 · 循证决策（商科统计）

UTS Business School's first-year statistics core — read data honestly, decide with evidence, and do it all in Excel.悉尼科大商学院大一统计核心课 —— 学会诚实地读懂数据、用证据做决策，并全程用 Excel 实操。

26134 is the Bachelor of Business statistics foundation (formerly "Business Statistics"). It teaches you to describe data, reason about probability and uncertainty, and run estimation, hypothesis tests and regression on real business questions — assessed by online quizzes and Excel-based project work, with no final exam. This guide is built from 69 real 26134 course materials in the AskSia Library.

26134 是悉尼科大商科（Bachelor of Business）的统计基础课（前身为 Business Statistics）。它教你描述数据、理解概率与不确定性，并用估计、假设检验、回归去回答真实商业问题 —— 考核由在线 quiz 和基于 Excel 的项目作业构成，没有期末闭卷考试。本指南基于AskSia Library 中的 69 份真实 26134 课程材料整理而成。

Built from 69 real 26134 course materials in the AskSia Library.

基于AskSia Library 中的 69 份真实 26134 课程材料整理而成。

Faculty院系UTS Business SchoolLevel层级Undergraduate · 1000-levelCredit学分6 ptsSemester学期2026 Semester 1Prereq先修NoneCampus校区City (Haymarket), Sydney
📚 AskSia Library data·69 AskSia Library resources·10 topics·100% coursework: online quizzes 20% (best 4 of 6) + Excel-based data-analysis project work — no central written exam.Built from 69 real 26134 course materials in the AskSia Library. 26134 has no final exam, so the practice below is assessment-style on the real statistics syllabus, not paraphrased exam questions.
📚 AskSia Library 数据·69 份 AskSia Library 资料·10 个主题·100% 平时考核：在线 quiz 20%（6 次取最好 4 次）+ 基于 Excel 的数据分析项目作业 —— 没有集中笔试。基于 AskSia Library 中的 69 份真实 26134 课程材料整理。26134 没有期末考试，因此下方练习是针对真实统计大纲的「考核风格」题，并非对真题的转述。
Overview课程概览

What 26134 is about26134 讲什么

26134 Responsible Evidence-Based Decisions is the introductory business-statistics subject in the UTS Bachelor of Business. It develops the ability to assess and critically interpret quantitative data from business and society within a framework of evidence-based reasoning. Students learn to explore and summarise data, work with probability and probability distributions, draw inferences through sampling, estimation and hypothesis testing, and model relationships with correlation and regression — performing the analysis in Microsoft Excel. The subject stresses interpreting results honestly and communicating defensible, data-driven conclusions for business and policy decisions.

26134 Responsible Evidence-Based Decisions 是悉尼科大商科本科的入门统计课。它在「循证推理」的框架下，培养学生评估并批判性解读来自商业与社会的定量数据的能力。学生将学习探索与汇总数据、掌握概率与概率分布、通过抽样·估计·假设检验进行推断，并用相关与回归对变量关系建模 —— 全部分析在 Microsoft Excel 中完成。课程强调诚实地解读结果，并就商业与政策决策给出有数据支撑、站得住脚的结论。

Topic map知识地图

The 26134 syllabus, topic by topic26134 大纲 · 逐个主题

1

Exploring & describing data数据探索与描述

Types of data (categorical vs numerical, nominal/ordinal, discrete/continuous), frequency tables, histograms, bar and pie charts. How to summarise a dataset before analysing it.

数据类型（分类 vs 数值、名义/有序、离散/连续）、频数表、直方图、条形图与饼图。分析前如何先把一组数据「看清楚」。

2

Measures of centre & spread集中趋势与离散程度

Mean, median and mode; variance, standard deviation and the coefficient of variation; skewness and distribution shape. Choosing the right summary measure for the data.

均值、中位数、众数；方差、标准差与变异系数；偏度与分布形态。针对不同数据选择合适的汇总指标。

3

Probability fundamentals概率基础

Sample spaces and events, the rules of probability, conditional probability and independence, and Bayes' reasoning. The logic of uncertainty that underpins inference.

样本空间与事件、概率法则、条件概率与独立性、贝叶斯推理。这是后续统计推断背后的「不确定性」逻辑。

4

Random variables & probability distributions随机变量与概率分布

Discrete distributions (e.g. binomial) and continuous distributions, centred on the normal distribution and standardising with z-scores.

离散分布（如二项分布）与连续分布，重点是正态分布，以及用 z 分数做标准化。

5

Sampling & sampling distributions抽样与抽样分布

Populations vs samples, sampling methods, the sampling distribution of the mean and the Central Limit Theorem — why sample statistics behave predictably.

总体 vs 样本、抽样方法、样本均值的抽样分布与中心极限定理 —— 解释为什么样本统计量会有可预测的规律。

6

Estimation & confidence intervals估计与置信区间

Point estimates and confidence intervals for a mean and a proportion, and how confidence level and sample size affect interval width.

对均值与比例的点估计与置信区间，以及置信水平与样本量如何影响区间宽度。

7

Hypothesis testing假设检验

Null and alternative hypotheses, significance levels, p-values, Type I/II errors, and one- and two-sample tests for means and proportions.

原假设与备择假设、显著性水平、p 值、第一类/第二类错误，以及对均值与比例的单样本、双样本检验。

8

Correlation & regression相关与回归

Scatterplots, correlation, simple linear regression, interpreting slope and R-squared, and using a fitted model to make and qualify predictions.

散点图、相关性、一元线性回归、解读斜率与 R²，并用拟合模型做出（且审慎限定）预测。

9

Data analysis in ExcelExcel 数据分析实操

Carrying out the above analyses in Microsoft Excel (formulas, charts, the Data Analysis ToolPak) — the practical data-analytic toolkit assessed in the project work.

在 Microsoft Excel 中完成上述分析（公式、图表、Data Analysis 工具库）—— 这正是项目作业所考核的实操数据分析能力。

10

Interpreting & communicating evidence证据的解读与沟通

Turning statistical output into honest, defensible conclusions for business and policy decisions, and recognising misuse of data and analytical pitfalls.

把统计结果转化为诚实、站得住脚的商业与政策结论，并识别数据误用与分析陷阱。

Assessment考核方式

How 26134 is assessed26134 怎么考核

Final exam: No期末考试：无
Component考核项 Weight占比 Note说明
Online quizzes (best 4 of 6)在线 quiz（取 6 次中最好的 4 次） 20% Six quizzes across the session, best four count. Multiple-choice and fill-in-the-blank drawn from a question bank, 1 hour, one attempt, completed within a multi-day release window.整学期共 6 次 quiz，取最好的 4 次计分。题目为题库抽取的选择题与填空题，限时 1 小时、仅一次作答，在多日开放窗口内完成。
Excel-based project / data-analysis report work基于 Excel 的项目 / 数据分析报告作业 ~80% (project / coursework) Excel-based data-analysis project/report work makes up the remainder. Exact weighting is in the current subject outline.基于 Excel 的数据分析项目 / 报告占其余部分。具体比例以当年 subject outline 为准。

No central written exam. Online quizzes 20% (best 4 of 6) + Excel-based data-analysis project/coursework (the remainder). Confirm exact project weighting on the current UTS subject outline.

没有集中笔试。在线测验 20%（6 次取最好 4 次）+ 基于 Excel 的数据分析项目 / 平时作业（其余部分）。具体项目比例以 UTS 当年 subject outline 为准。

Assessment timeline考核时间线

When each 26134 task is due26134 各项考核时间

Online quizzes (best 4 of 6)在线 quiz（6 次取最好 4 次）
Continuous across the session (6 quizzes in release windows)整学期持续进行（6 次 quiz，各有开放窗口）
20%
Excel-based data-analysis project / report work基于 Excel 的数据分析项目 / 报告作业
During session (coursework; exact due dates in the current subject outline)学期内进行（平时作业；具体截止日以当年 subject outline 为准）
~80% (project / coursework)
Self-test自测练习

Test yourself: 26134 practice questions自测一下：26134 练习题

Question 1第 1 题
A retailer records the postcode of each customer along with the dollar amount of their last purchase. How are these two variables best classified?一家零售商记录了每位顾客的邮编以及最近一次消费的金额。这两个变量最准确的分类是？
  1. Postcode is numerical, purchase amount is categorical
  2. Both are numerical
  3. Postcode is categorical (nominal), purchase amount is numerical (continuous)
  4. Both are categorical
  1. 邮编是数值型，消费金额是分类型
  2. 两者都是数值型
  3. 邮编是分类型（名义），消费金额是数值型（连续）
  4. 两者都是分类型
Show answer查看答案
Answer: C. Postcode is categorical (nominal), purchase amount is numerical (continuous)Although a postcode looks like a number, it only labels a location and has no meaningful order or arithmetic, so it is categorical (nominal). Dollars spent can take any value on a scale and is numerical continuous. Picking the right data type drives which chart and summary you may legitimately use.
答案：C. 邮编是分类型（名义），消费金额是数值型（连续）邮编虽然写成数字，但它只是地点的标签，没有有意义的大小或运算，所以是分类（名义）变量；消费金额可在连续刻度上取值，属于数值连续变量。正确判断数据类型，才能决定可以合法使用哪种图表和汇总指标。
Question 2第 2 题
A sample of weekly sales is strongly right-skewed by a few very large weeks. Which measure best describes the 'typical' week, and why?一组周销售额样本因少数几个极大值而严重右偏。哪个指标最能代表「典型」一周，为什么？
  1. The mean, because it uses every value
  2. The median, because it is resistant to the extreme high values
  3. The mode, because it is the most common value
  4. The standard deviation, because it measures the centre
  1. 均值，因为它用到了每一个数据
  2. 中位数，因为它不受极大值的影响
  3. 众数，因为它是出现最多的值
  4. 标准差，因为它衡量中心
Show answer查看答案
Answer: B. The median, because it is resistant to the extreme high valuesIn a right-skewed distribution the few large values pull the mean upward, so the mean overstates the typical week. The median sits at the middle rank and is resistant to extreme values, so it better represents the centre. The standard deviation measures spread, not centre — that option is a category error.
答案：B. 中位数，因为它不受极大值的影响在右偏分布中，少数极大值把均值往上拉，使均值高估了「典型」一周。中位数位于排序中点、不受极端值影响，更能代表中心。标准差衡量的是离散程度而非中心 —— 该选项属于概念错位。
Question 3第 3 题
A 95% confidence interval for mean monthly spend is [$180, $220]. Which statement is the correct interpretation?对月均消费的 95% 置信区间为 [$180, $220]。哪种解读是正确的？
  1. 95% of customers spend between $180 and $220
  2. There is a 95% probability the true mean is exactly $200
  3. We are 95% confident the interval [$180, $220] captures the true population mean spend
  4. If we resample, 95% of individual customers will fall in this interval
  1. 95% 的顾客消费在 $180 到 $220 之间
  2. 真实均值恰好为 $200 的概率是 95%
  3. 我们有 95% 的把握，区间 [$180, $220] 涵盖了真实的总体均值消费
  4. 若重新抽样，95% 的单个顾客会落在此区间内
Show answer查看答案
Answer: C. We are 95% confident the interval [$180, $220] captures the true population mean spendA confidence interval is a statement about the population mean, not about individual customers. The correct reading is that the procedure captures the true mean in 95% of repeated samples — i.e. we are 95% confident this interval contains the population mean. It does not say 95% of customers, nor give a probability for one fixed (already-determined) parameter value.
答案：C. 我们有 95% 的把握，区间 [$180, $220] 涵盖了真实的总体均值消费置信区间是关于总体均值的陈述，而不是关于单个顾客的。正确解读是：在重复抽样中，该方法有 95% 的次数会涵盖真实均值 —— 即我们有 95% 的把握这个区间包含总体均值。它既不是说 95% 的顾客，也不是给某个已固定的参数值赋概率。
Assessment-style questions考核题型

Key assessment-style questions in 2613426134 核心考核风格题

Exploring & describing data数据探索与描述
Given a small dataset or a frequency table, classify each variable's data type and choose/justify the appropriate chart or summary; spot a misleading chart.给定一个小数据集或频数表，判断每个变量的数据类型并选择/论证合适的图表或汇总指标；指出有误导性的图表。
Assessment-style on the real syllabus (no final exam in 26134).
Measures of centre & spread集中趋势与离散程度
Compute or read off mean/median/SD and decide which summary is appropriate given skew or outliers; interpret the coefficient of variation when comparing two datasets.计算或读出均值/中位数/标准差，并在存在偏态或离群值时判断该用哪个汇总指标；用变异系数比较两组数据。
Assessment-style on the real syllabus.
Probability fundamentals概率基础
Apply probability rules, conditional probability and independence to a business scenario (often via a two-way / contingency table); a Bayes-style update on a screening or test situation.在商业情境中应用概率法则、条件概率与独立性（常借助双向 / 列联表）；对筛查或检测情境做贝叶斯式更新。
Assessment-style on the real syllabus.
Random variables & distributions随机变量与概率分布
Use a binomial model to find a probability of k successes; standardise with a z-score and find a normal-distribution probability or percentile for a business quantity.用二项模型求 k 次成功的概率；用 z 分数标准化，求某商业量的正态分布概率或分位数。
Assessment-style on the real syllabus.
Sampling & sampling distributions抽样与抽样分布
Apply the Central Limit Theorem to a sample mean: compute the standard error and a probability about the sample mean; explain why the sampling distribution is approximately normal.对样本均值应用中心极限定理：计算标准误与关于样本均值的概率；解释抽样分布为何近似正态。
Assessment-style on the real syllabus.
Estimation & confidence intervals估计与置信区间
Construct a confidence interval for a mean or a proportion; interpret it correctly and explain how confidence level and sample size change the interval width.构建均值或比例的置信区间；正确解读，并说明置信水平与样本量如何改变区间宽度。
Assessment-style on the real syllabus.
Hypothesis testing假设检验
State H0/H1 for a business claim, run a one- or two-sample test for a mean or proportion, and decide using the p-value vs significance level; identify Type I/II error in context.为某商业主张写出 H0/H1，对均值或比例做单样本或双样本检验，用 p 值与显著性水平做判断；在情境中识别第一类/第二类错误。
Assessment-style on the real syllabus.
Correlation & regression相关与回归
Interpret a regression output (slope, intercept, R-squared, significance) for a business relationship and use the model to make and qualify a prediction; distinguish correlation from causation.解读某商业关系的回归输出（斜率、截距、R²、显著性），并用模型做出（且审慎限定）预测；区分相关与因果。
Assessment-style on the real syllabus.
Data analysis in ExcelExcel 数据分析实操
Reproduce an analysis in Excel (formulas, charts, the Data Analysis ToolPak) and read the output — the practical skill the project work assesses.在 Excel 中复现一项分析（公式、图表、Data Analysis 工具库）并读懂输出 —— 这正是项目作业考核的实操能力。
Assessment-style on the real syllabus.
Interpreting & communicating evidence证据的解读与沟通
Turn statistical output into an honest, defensible business/policy conclusion and flag the analytical pitfalls or data misuse that would undermine it.把统计结果转化为诚实、站得住脚的商业/政策结论，并指出会动摇该结论的分析陷阱或数据误用。
Assessment-style on the real syllabus.
Key terms核心术语

26134 glossary26134 术语表

Descriptive statistics描述性统计
Methods that summarise and describe the main features of a dataset (centre, spread, shape) without generalising beyond it.
用来汇总并描述一组数据主要特征（集中趋势、离散程度、形态）的方法，不对数据之外做推断。
Standard deviation标准差
A measure of how spread out values are around the mean, in the same units as the data.
衡量数据相对均值离散程度的指标，单位与原数据相同。
Probability distribution概率分布
A rule giving the probabilities of the possible values of a random variable (e.g. binomial, normal).
描述随机变量各可能取值对应概率的规则（如二项分布、正态分布）。
Normal distribution正态分布
The symmetric bell-shaped continuous distribution, central to inference; values are standardised using z-scores.
对称的钟形连续分布，是统计推断的核心；其取值用 z 分数标准化。
Central Limit Theorem中心极限定理
The result that the sampling distribution of the mean approaches a normal distribution as sample size grows, whatever the population shape.
无论总体形态如何，随着样本量增大，样本均值的抽样分布趋近正态分布的定理。
Confidence interval置信区间
A range of plausible values for a population parameter, with a stated confidence level (e.g. 95%).
在给定置信水平（如 95%）下，对总体参数的一段可信取值范围。
p-valuep 值
The probability of observing data as extreme as yours if the null hypothesis were true; small p-values argue against the null.
在原假设为真时，观测到与现有数据一样极端结果的概率；p 值越小越不支持原假设。
Hypothesis test假设检验
A procedure that uses sample evidence to decide between a null and an alternative claim about a population.
利用样本证据，在关于总体的原假设与备择假设之间做出判断的过程。
Linear regression线性回归
Fitting a straight line to model how one variable depends on another, for explanation and prediction.
用一条直线建模一个变量对另一个变量的依赖关系，用于解释与预测。
R-squaredR²（决定系数）
The proportion of variation in the outcome explained by the regression model, from 0 to 1.
回归模型所解释的因变量变异比例，取值 0 到 1。
Evidence-based decision循证决策
A decision justified by data and analysis rather than intuition alone — the organising theme of 26134.
以数据与分析（而非仅凭直觉）为依据的决策 —— 26134 的核心主题。
Data Analysis ToolPak (Excel)Excel 数据分析工具库
An Excel add-in providing statistical routines (descriptives, t-tests, regression) used for the subject's project work.
Excel 的一个加载项，提供描述统计、t 检验、回归等统计功能，用于本课程的项目作业。
FAQ

26134 — common questions26134 常见问题

How is 26134 assessed — is there a final exam?26134 怎么考核 —— 有期末考试吗？
There is no final exam. 26134 is 100% coursework: regular online quizzes (best 4 of your 6 quizzes, around 20%) plus Excel-based data-analysis project work for the remaining weight. The quizzes are multiple-choice / fill-in-the-blank from a bank, 1 hour, one attempt, completed inside a release window. Confirm the exact task splits in your current Subject Outline on Canvas.
没有期末考试。26134 是 100% 平时考核：定期的在线 quiz（取 6 次中最好的 4 次，约 20%），加上基于 Excel 的数据分析项目作业承担其余权重。Quiz 为题库抽取的选择题/填空题，限时 1 小时、仅一次作答，需在开放窗口内完成。具体任务拆分以 Canvas 当学期 Subject Outline 为准。
Is 26134 the same as "Business Statistics"?26134 就是以前的「Business Statistics」吗？
Yes. 26134 was previously titled "Business Statistics" and is now "Responsible Evidence-Based Decisions." The code, the statistics syllabus and the Excel-based, evidence-based-reasoning focus are the same; only the name was updated.
是的。26134 原名「Business Statistics」（商科统计），现更名为「Responsible Evidence-Based Decisions」。课程代码、统计大纲，以及以 Excel 实操、循证推理为核心的定位都没变，只是改了名字。
Do I need maths or stats background, and are there prerequisites?需要数学/统计基础吗？有先修课吗？
There is no listed prerequisite — it's a first-year (1000-level) core subject in the UTS Bachelor of Business that starts from the basics. Note it is an antirequisite of 26133 Business Information Analysis, so you cannot take both for credit. Comfort with Excel helps, since the project work is done in Excel.
没有列出的先修课 —— 它是悉尼科大商科本科大一（1000 级）核心课，从基础讲起。注意它与 26133 Business Information Analysis 互为 antirequisite，二者只能选其一计学分。会用 Excel 会有帮助，因为项目作业都在 Excel 里完成。
What software does 26134 use?26134 用什么软件？
Microsoft Excel. The subject deliberately builds data-analytic capability in Excel — descriptive statistics, charts, and tools like the Data Analysis ToolPak — rather than a specialist stats package, so the skills transfer directly to business workplaces.
Microsoft Excel。课程刻意用 Excel 来培养数据分析能力 —— 描述统计、图表，以及 Data Analysis 工具库等 —— 而不是专门的统计软件，因此技能能直接迁移到商业工作场景。
Can I use AskSia to study for 26134, and is that allowed?可以用 AskSia 备考 26134 吗？这被允许吗？
Yes. Sia works as a tutor — it explains concepts (e.g. what a p-value means), walks through Excel steps, and checks your reasoning on practice problems. That fits UTS's policy on AI-assisted study. Submitting Sia-generated output as your own quiz or project work is academic misconduct under UTS policy — use it to learn, not to substitute your own work.
可以。Sia 相当于一位 tutor —— 帮你讲清概念（比如 p 值到底是什么）、带你走 Excel 步骤、检查你做练习题的思路，这符合悉尼科大的 AI 辅助学习政策。但把 Sia 生成的内容当作自己的 quiz 或项目作业提交，按悉尼科大政策属于学术不端 —— 它用来学习，不能替代你自己的作业。
More at UTS更多 悉尼科大 课程

Other UTS course guides悉尼科大 其他课程指南

21212 at UTS· 悉尼科大
All UTS courses全部 悉尼科大 课程 Browse all AU courses浏览全部澳洲课程

AskSia is an independent study aid and is not affiliated with, endorsed by, or sponsored by University of Technology Sydney. Course details may change — always confirm against the official handbook. Read about how this guide is built. AskSia 是独立的学习辅助工具，与悉尼科技大学没有任何隶属、背书或赞助关系。课程信息可能变动，请始终以官方 handbook 为准。了解本指南的编写方法。